Using the Low-Pass Monogenic Signal Framework for Cell/Background Classification on Multiple Cell Lines in Bright-Field Microscope Images - Mualla, Firas; Schöll, Simon; Sommerfeldt, Björn; Maier, Andreas; Steidl, Stefan; Buchholz, Rainer; Hornegger, Joachim (International Journal of Computer Assisted Radiology and Surgery, 2014)
Purpose: Several cell detection approaches which deal with bright-field microscope images utilize defocusing to increase image contrast. The latter is related to the physical light phase through the transport of intensity equation (TIE). Recently, it was shown that it is possible to approximate the solution of the TIE using a low-pass monogenic signal framework. The purpose of this paper is to show that using the local phase of the aforementioned monogenic signal instead of the defocused image improves the cell/background classification accuracy. Materials and methods: The paper statement was tested on an image database composed of three cell lines: adherent CHO, adherent L929, and Sf21 in suspension. Local phase and local energy images were generated using the low-pass monogenic signal framework with axial derivative images as input. Machine learning was then employed to investigate the discriminative power of the local phase. Three classifier models were utilized: random forest (RF), support vector machine (SVM) with a linear kernel, and SVM with a radial basis function (RBF) kernel. Results: The improvement, averaged over cell lines, of classifying 5 x 5 sized patches extracted from the local phase image instead of the defocused image was 7.3 % using the RF, 11.6 % using the linear SVM, and 10.2 % when a RBF kernel was employed instead of the linear one. Furthermore, the feature images can be sorted by increasing discriminative power as follows: at-focus signal, local energy, defocused signal, local phase. The only exception to this order was the superiority of local energy over defocused signal for suspended cells. Conclusions: Local phase computed using the low-pass monogenic signal framework considerably outperforms the defocused image for the purpose of pixel-patch cell/background classification in bright-field microscopy. © 2013 CARS.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.usingt
Keywords: Bright-field microscopy; Cell detection; Local phase; Machine learning; Monogenic signal; Transport of intensity equation

Validation of a classification and scoring system for the diagnosis of laryngeal and pharyngeal squamous cell carcinomas by confocal laser endomicroscopy - Sievert, Matti; Mantsopoulos, Konstantinos; Müller-Hübner, Sarina; Rupp, Robin; Eckstein, Markus; Stelzle, Florian; Oetter, Nicolai; Maier, Andreas; Aubreville, Marc; Iro, Heinrich; Goncalves, Miguel (Brazilian Journal of Otorhinolaryngology, 2021)
Introduction: Confocal laser endomicroscopy is an optical imaging technique that allows in vivo, real-time, microscope-like images of the upper aerodigestive tract's mucosa. The assessment of morphological tissue characteristics for the correct differentiation between healthy and malignant suspected mucosa requires strict evaluation criteria. Objective: This study aims to validate an eight-point score for the correct assessment of malignancy. Methods: We performed confocal laser endomicroscopy between March and October 2020 in 13 patients. 197 sequences (11.820 images) originated from the marginal area of pharyngeal and laryngeal carcinomas. Specimens were taken at corresponding locations and analyzed in H&E staining as a standard of reference. A total of six examiners evaluated the sequences based on a scoring system; they were blinded to the histopathological examination. The primary endpoints are sensitivity, specificity, and accuracy. Secondary endpoints are interrater reliability and receiver operator characteristics. Results: Healthy mucosa showed epithelium with uniform size and shape with distinct cytoplasmic membranes and regular vessel architecture. Confocal laser endomicroscopy of malignant cells demonstrated a disorganized arrangement of variable cellular morphology. We calculated an accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 83.2%, 81.3%, 85.5%, 86.7%, and 79.7%, respectively, with a κ-value of 0.64, and an area under the curve of 0.86. Conclusion: The results confirm that this scoring system is applicable in the laryngeal and pharyngeal mucosa to classify benign and malignant tissue. A scoring system based on defined and reproducible characteristics can help translate this experimental method to broad clinical practice in head and neck diagnosis.
CRIS-Team Scopus Importer:2021-08-13
Keywords: Confocal laser endomicroscopy; Diagnosis; Head and neck cancer; Squamous cell carcinoma

Validation of a Deep Learning based Automated Myocardial Inversion Time Selection for Late Gadolinium Enhancement Imaging in a Prospective Study - Yoon, Seung Su; Schmidt, Michaela; Rick, Manuela; Chitiboi, Teodora; Sharma, Puneet; Emrich, Tilman; Tillmanns, Christoph; Waßmuth, Ralph; Wetzl, Jens; Maier, Andreas (International Society for Magnetic Resonance in Medicine (ISMRM) 29th Annual Meeting & Exhibition, 2021)

Validity of tissue homogeneity in confocal laser endomicroscopy on the diagnosis of laryngeal and hypopharyngeal squamous cell carcinoma - Sievert, Matti; Aubreville, Marc; Gostian, Antoniu-Oreste; Mantsopoulos, Konstantinos; Koch, Michael; Müller-Hübner, Sarina; Eckstein, Markus; Rupp, Robin; Stelzle, Florian; Oetter, Nicolai; Maier, Andreas; Iro, Heinrich; et al. (European Archives of Oto-Rhino-Laryngology, 2022)
Purpose: Confocal laser endomicroscopy (CLE) allows imaging of the laryngeal mucosa in a thousand-fold magnification. This study analyzes differences in tissue homogeneity between healthy mucosa and squamous cell carcinoma (SCC) via CLE. Materials and methods: We included five SCC patients with planned total laryngectomy in this study between October 2020 and February 2021. We captured CLE scans of the tumor and healthy mucosa. Analysis of image homogeneity to diagnose SCC was performed by measuring the signal intensity in four regions of interest (ROI) in each frame in a total of 60 sequences. Each sequence was assigned to the corresponding histological pattern, derived from hematoxylin and eosin staining. In addition, we recorded the subjective evaluation of seven investigators regarding tissue homogeneity. Results: Out of 3600 images, 1620 (45%) correlated with benign mucosa and 1980 (55%) with SCC. ROIs of benign mucosa and SCC had a mean and standard deviation (SD) of signal intensity of, respectively, 232.1 ± 3.34 and 467.3 ± 9.72 (P < 0.001). The mean SD between the four different ROIs was 39.1 ± 1.03 for benign and 101.5 ± 2.6 for SCC frames (P < 0.001). In addition, homogeneity yielded a sensitivity and specificity of 81.8% and 86.2%, respectively, regarding the investigator-dependent analysis. Conclusions: SCC shows a significant tissue inhomogeneity in comparison to the healthy epithelium. The results support this feature’s importance in identifying malignant mucosa areas during CLE examination. However, the examiner-dependent evaluation emphasizes that homogeneity is a sub-criterion that must be considered in a broad context.
CRIS-Team Scopus Importer:2022-03-18
Keywords: Classification system; Confocal laser endomicroscopy; Head and neck cancer; Larynx; Non-invasive histological imaging; Pharynx

Variable Interscan Time Analysis (VISTA-) Optical Coherence Tomography Angiography (OCTA) of Blood Flow Speeds in Eyes with Diabetic Retinopathy - Moult, Eric M.; Ploner, Stefan; Rebhun, Carl B.; Alibhai, A. Yasin; Lee, Byungkun; Moreira-Neto, Carlos; Novais, Eduardo A.; Schottenhamml, Julia; Husvogt, Lennart; Maier, Andreas; Rosenfeld, Philip; Duker, Jay S.; et al. (Investigative Ophthalmology & Visual Science, 2017)
Purpose : The introduction of optical coherence tomography angiography (OCTA) allows clinicians to study the microvascular alterations that occur in diabetic retinopathy (DR). Standard OCTA systems yield a detailed picture of the presence of vasculature, but have limited dynamic range and provide little information about blood flow speeds. Our group has recently developed variable interscan time analysis (VISTA), which provides a framework for studying relative blood flow speeds using OCTA. This study investigates VISTA to assess microvascular blood flow speeds in eyes with DR. Methods : Using a VISTA protocol we analyzed 3 eyes with mild non-proliferative DR (NPDR) (2 patients; 66.7±2.31 y/o), 5 eyes with moderate NPDR (3 patients; 57.6±16.58 y/o), 5 eyes with severe NPDR (4 patients; 51.8±13.44 y/o), and 8 eyes with proliferative DR (PDR) (6 patients; 57.0±13.38 y/o). Results : We observed a range of flow distributions associated with capillary looping/microaneurysms, tortuous vessels, dilated vessels, and neovasculature vessels. Representative VISTA-OCTA images are shown in Figures 1 and 2. Conclusions : VISTA is a useful framework for studying blood flow speeds in eyes with D
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.IMMD5.variab
Keywords: oct; octa; oct angiography; vista; variable interscan time analysis; blood flow; diabetic retinopathy

Velo-CT – A Collaborative 3D Racing Game as Incentive for Medical Image Annotation - Sukesh, Richin; Goshika, Sai Kishore; Narayanan, Srividhya Sathya; Bhombore, Vineet Vinay; Fu, Weilin; Kordon, Florian Johannes; Maier, Andreas (HEALTHINF - 14th International Conference on Health Informatics, 2021)
Introduction Recent advances in algorithmic processing of medical data are largely based on the use of deep learning algorithms that rely on large amounts of annotated data [1]. To cope with the often complex and time-consuming nature of data acquisition and annotation, data donation and distributed crowd-labeling are an appealing approach [2]. In this paper, we follow this idea and present a single/multi-player mobile game which aids in the annotation process of CT or MR scan data. Methods To provide users with an exciting and rewarding annotation experience, our game "Velo-CT" integrates labeling of 3D volume slices into a classical racing game. This is achieved by using data annotation as a key to unlock more advanced racing tracks in the game. To this end, the game has two game modes: 1) Track Racing, and 2) Image Annotation. In “Track Racing”, players compete by steering a vehicle through a racetrack. The main task is to overtake the opponents and try to remain first in position until the end of the race. Checkpoints along the track allow additional guidance if players accidentally leave the racetrack. To provide a more challenging and diverse experience, respawning pick-ups can be found at random locations along the track in the form of floating CT scanners. Such pick-up can either be a speed multiplier (multiplies car speed by 1.2) or a size enhancer (increases the car size by 1.5 to threat opponents and impede their view). The racetracks are modeled based on the segmentation contour of clinical CT data, e.g. showing the respiratory region. For slice segmentation we use a simple K-means classifier. Annotation points earned after beating a track allow the player to unlock new data for the second game mode “Image Annotation”. This is where the medical expertise of the player comes into play. Based on the current progress, the player is presented several medical images which may contain abnormalities. If an abnormality is spotted, the precise location can be marked by a finger tap on the area. The more images a player annotates, the more points can be earned to unlock a new track – and eventually a more intricate anatomy to race on. Results Our game was prototyped in Unity3D [3]. As a first anatomy, respiratory tracks based on the “Low Dose CT Grand Challenge” data were constructed. First experiences by players of different gaming background were reported for the single player setting. Users report a fast-paced racing experience with a healthy amount of challenge provided by pick-ups and competitive AI opponents. The organ-aligned layouts result in tracks similar in shape, which should be addressed in the future by adding multiple anatomical regions. Conclusion Velo-CT provides an intuitive and extendable integration of the often tedious data annotation process for 3D medical data into a rewarding gaming context. The prototype is available for free download and serves as a basis for further extensions to related game modes. References [1] Maier, A., Syben, C., Lasser, T., & Riess, C. (2019). A gentle introduction to deep learning in medical image processing. Zeitschrift für Medizinische Physik, 29(2), 86-101. [2] Servadei, L., Schmidt, R., Eidelloth, C., & Maier, A. (2017, October). Medical Monkeys: A Crowdsourcing Approach to Medical Big Data. In OTM Confederated International Conferences" On the Move to Meaningful Internet Systems" (pp. 87-97). Springer, Cham. [3] Murray, J. W. (2014). C# game programming cookbook for Unity 3D. AK Peters/CRC Press. <br
Keywords: Gamification; Medical Image Annotation; Deep Learning.

Verbesserung der Deep Learning basierten Autodetektion von Hirnmetastasen durch Integration von MRT-Voraufnahmen - Huang, Yixing; Bert, Christoph; Sommer, Philipp; Frey, Benjamin; Gaipl, Udo; Distel, Luitpold; Weissmann, Thomas; Uder, Michael; Schmidt, Manuel; Dörfler, Arnd; Maier, Andreas; Fietkau, Rainer; et al. (Strahlentherapie und Onkologie, 2022)
Keywords: Deep learning; brain metastasis

Verstaendlichkeit von Kindern mit unterschiedlichen orofazialen Spaltfehlbildungen - Schuster, Maria; Maier, Andreas; Schützenberger, Anne; Nkenke, Emeka; Holst, Alexandra; Rosanowski, Frank; Eysholdt, Ulrich (Aktuelle phoniatrische Aspekte 2008, 2008)

Verständlichkeitsanalyse bei Kindern mit Lippen-Kiefer-Gaumenspalten auf Deutsch und Italienisch mittels automatischer Spracherkennung - Maier, Andreas; Bocklet, Tobias; Scipioni, Marcello; Nöth, Elmar; Eysholdt, Ulrich; Schuster, Maria (Aktuelle phoniatrisch-pädaudiologische Aspekte 2009, 2009)

Vesselness for Text Detection in Historical Document Images - Hofmann, Simon; Gropp, Martin; Bernecker, David; Pollin, Christopher; Maier, Andreas; Christlein, Vincent (2016 IEEE International Conference on Image Processing (ICIP), 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.vessel
Keywords: Historical document analysis; RootSIFT; Text detection; Vesselness; VLAD

View-Consistent Metal Segmentation in the Projection Domain for Metal Artifact Reduction in CBCT – An Investigation of Potential Improvement - Gottschalk, Tristan; Maier, Andreas; Kordon, Florian Johannes; Kreher, Björn W. (Journal Machine Learning for Biomedical Imaging, 2021)
The positive outcome of a trauma intervention depends on an intraoperative evaluation of inserted metallic implants. Due to occurring metal artifacts, the quality of this evaluation heavily depends on the performance of so-called Metal Artifact Reduction methods (MAR). The majority of these MAR methods require prior segmentation of the inserted metal objects. Therefore, typically a rather simple thresholding-based segmentation method in the reconstructed 3D volume is applied, despite some major disadvantages. With this publication, the potential of shifting the segmentation task to a learning-based, view-consistent 2D projection-based method on the downstream MAR's outcome is investigated. For segmenting the present metal, a rather simple learning-based 2D projection-wise segmentation network that is trained using real data acquired during cadaver studies, is examined. To overcome the disadvantages that come along with a 2D projection-wise segmentation, a Consistency Filter is proposed. The influence of the shifted segmentation domain is investigated by comparing the results of the standard fsMAR with a modified fsMAR version using the new segmentation masks. With a quantitative and qualitative evaluation on real cadaver data, the investigated approach showed an increased MAR performance and a high insensitivity against metal artifacts. For cases with metal outside the reconstruction's FoV or cases with vanishing metal, a significant reduction in artifacts could be shown. Thus, increases of up to roughly 3 dB w.r.t. the mean PSNR metric over all slices and up to 9 dB for single slices were achieved. The shown results reveal a beneficial influence of the shift to a 2D-based segmentation method on real data for downstream use with a MAR method, like the fsMAR. The nature of the method further suggests the same beneficial behavior for all (also recent data-driven) MAR methods, that for now comprise a 3D-volume-based segmentation step for subsequent inpainting.<br
Keywords: trauma intervention; cone-beam computed tomography; metal artifact reduction; metal segmentation

Viewpoint Planning for Quantitative Coronary Angiography - Preuhs, Alexander; Berger, Martin; Bauer, Sebastian; Redel, Thomas; Unberath, Mathias; Achenbach, Stephan; Maier, Andreas (Computer Assisted Radiology and Surgery Proceedings of the 32nd International Congress and Exhibition, 2018)
Purpose: In coronary angiography, the condition of myocardial blood supply is assessed by analyzing 2-D X-ray projections of contrasted coronary arteries. This is done using a flexible C-arm system. Due to the X-ray immanent dimensionality reduction projecting the 3-D scene onto a 2-D image, the viewpoint is critical to guarantee an appropriate view onto the affected artery and, thus, enable reliable diagnosis. In this work, we introduce an algorithm computing optimal viewpoints for the assessment of coronary arteries without the need for 3-D models. Methods: We introduce the concept of optimal viewpoint planning solely based on a single angiographic X-ray image. The subsequent viewpoint is computed such that it is rotated precisely around a vessel, while minimizing foreshortening. Results: Our algorithm reduces foreshortening substantially compared to the input view and completely eliminates it for 90 ∘ rotations. Rotations around isocentered foreshortening-free vessels passing the isocenter are exact. The precision, however, decreases when the vessel is off-centered or foreshortened. We evaluate worst-case boundaries, providing insight in the maximal inaccuracies to be expected. This can be utilized to design viewpoints guaranteeing desired requirements, e.g., a true rotation around the vessel of at minimum 30 ∘. In addition, a phantom study is performed investigating the impact of input views to 3-D quantitative coronary angiography (QCA). Conclusion: We introduce an algorithm for optimal viewpoint planning from a single angiographic X-ray image. The quality of the second viewpoint—i.e., vessel foreshortening and true rotation around vessel—depends on the first viewpoint selected by the physician; however, our computed viewpoint is guaranteed to reduce the initial foreshortening. Our novel approach uses fluoroscopy images only and, thus, seamlessly integrates with the current clinical workflow for coronary assessment. In addition, it can be implemented in the QCA workflow without increasing user interaction, making vessel-shape reconstruction more stable by standardizing viewpoints.
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.viewpo_9
Keywords: Active vision; C-arm; Coronary angiography; Foreshortening; Interventional imaging; Patient-specific imaging; QCA

Viewpoint Planning for Quantitative Coronary Angiography (IJCARS) - Preuhs, Alexander; Berger, Martin; Bauer, Sebastian; Redel, Thomas; Unberath, Mathias; Achenbach, Stephan; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.viewpo_5
Keywords: coronary angiography; C-arm; interventional imaging; QCA; active vision; patient specific imaging; foreshortening

Virtual cleaning and unwrapping of non-invasively digitized soiled bamboo scrolls - Stromer, Daniel; Christlein, Vincent; Huang, Xiaolin; Zippert, Patrick; Hausotte, Tino; Maier, Andreas (Scientific Reports, 2019)
In ancient China, symbols and drawings captured on bamboo and wooden slips were used as main communication media. Those documents are very precious for cultural heritage and research, but due to aging processes, the discovered pieces are sometimes in a poor condition and contaminated by soil. Manual cleaning of excavated slips is a demanding and time-consuming task in which writings can be accidentally deleted. To counter this, we propose a novel approach based on conventional 3-D X-ray computed tomography to digitize such historical documents without before manual cleaning. By applying a virtual cleaning and unwrapping algorithm, the entire scroll surface is remapped into 2-D such that the hidden content becomes readable. We show that the technique also works for heavily soiled scrolls, enabling an investigation of the content by the naked eye without the need for manual labor. This digitization also allows for recovery of potentially erased writings and reconstruction of the original spatial information.
CRIS-Team WoS Importer:2019-03-06

VirtualDSA++: Automated Segmentation, Vessel Labeling, Occlusion Detection and Graph Search on CT-Angiography Data - Thamm, Florian; Jürgens, Markus; Ditt, Hendrik; Maier, Andreas (Eurographics Workshop on Visual Computing for Biology and Medicine, 2020)
Computed Tomography Angiography (CTA) is one of the most commonly used modalities in the diagnosis of cerebrovascular diseases like ischemic strokes. Usually, the anatomy of interest in ischemic stroke cases is the Circle of Willis and its peripherals, the cerebral arteries, as these vessels are the most prominent candidates for occlusions. The diagnosis of occlusions in these vessels remains challenging, not only because of the large amount of surrounding vessels but also due to the large number of anatomical variants. We propose a fully automated image processing and visualization pipeline, which provides a full segmentation and modelling of the cerebral arterial tree for CTA data. The model itself enables the interactive masking of unimportant vessel structures e.g. veins like the Sinus Sagittalis, and the interactive planning of shortest paths meant to be used to prepare further treatments like a mechanical thrombectomy. Additionally, the algorithm automatically labels the cerebral arteries (Middle Cerebral Artery left and right, Anterior Cerebral Artery short, Posterior Cerebral Artery left and right) detects occlusions or interruptions in these vessels. The proposed pipeline does not require a prior non-contrast CT scan and achieves a comparable segmentation appearance as in a Digital Subtraction Angiography (DSA).<br
Keywords: Computed Tomography; Image Segmentation; Cerebrovascular Systems; Visualisation

Virtual Heart Models Help Elucidate the Role of Border Zone in Sustained Monomorphic Ventricular Tachycardia - Castaneda Gavina, Eduardo; Suzuki, Masahito; Ashikaga, Hiroshi; Lluch, Èric; Meister, Felix; Mihalef, Viorel; Audigier, Chloé; Maier, Andreas; Halperin, Henry; Passerini, Tiziano (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023)
Post-ischemic Ventricular Tachycardia (VT) is sustained by a depolarization wave re-entry through channel-like structures within the post-ischemic scar. These structures are usually formed by partially viable tissue, called Border Zone (BZ). Understanding the anatomical and electrical properties of the BZ is crucial to guide ablation therapy to the right targets, reducing the likelihood of VT recurrence. Virtual Heart methods can provide ablation guidance non-invasively, but they have high computational complexity and have shown limited capability to accurately reproduce the specific mechanisms responsible for clinically observed VT. These outstanding challenges undermine the utility of Virtual Hearts for high precision ablation guidance in clinical practice. In this work, fast phenomenological models are developed to efficiently and accurately simulate the re-entrant dynamics of VT as observed in 12-lead ECG. Two porcine models of Myocardial Infarction (MI) are used to generate personalized bi-ventricular models from pre-operative LGE-MRI images. Myocardial conductivity and action potential duration are estimated using sinus rhythm ECG measurements. Multiple hypotheses for the BZ tissue properties are tested, and optimal values are identified. These allow the Virtual Heart model to produce VTs with good agreements with measurements in terms of ECG lead polarity and VT cycle length. Efficient GPU implementation of the cardiac electrophysiology model allows computation of sustained monomorphic VT in times compatible with the clinical workflow.
CRIS-Team Scopus Importer:2023-11-03
Keywords: Cardiac modeling; ECG simulation; VAAT; VT

Virtual Single-frame Subtraction Imaging - Unberath, Mathias; Aichert, André; Achenbach, Stephan; Maier, Andreas (Proceedings of the 4th CT Meeting, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.virtua_8

Visualisierung der Differenzierung von Stimm- und Sprechbefunden - Maier, Andreas; Haderlein, Tino; Nöth, Elmar; Rosanowski, Frank; Eysholdt, Ulrich; Schuster, Maria (Aktuelle phoniatrische Aspekte 2008, 2008)

Visualization of Intelligibility Measured by Language-Independent Features - Haderlein, Tino; Middag, Catherine; Maier, Andreas; Martens, Jean-Pierre; Döllinger, Michael; Nöth, Elmar (Proc. Text, Speech and Dialogue; 17th International Conference, TSD 2014, 2014)
Automatic intelligibility assessment using automatic speech recognition is usually language-specific. In this study, a language-independent approach based on alignment-free phonological and phonemic features is proposed. It utilizes models that are trained with Flemish speech, and it is applied to assess dysphonic German speakers. In order to visualize the results, two techniques were tested: a plain selection of most relevant features emerging from Ensemble Linear Regression involving feature selection, and a Sammon transform of all the features to a 3-D space. The test data comprised recordings of 73 hoarse persons (48.3 ± 16.8 years) who read the German version of the text "The North Wind and the Sun". The reference evaluation was obtained by five speech therapists and physicians who rated intelligibility according to a 5-point Likert scale. In the 3-D visualization, the different levels of intelligibility were clearly separated. This could be the basis for an objective support for diagnostics in voice and speech rehabilitation. © 2014 Springer International Publishing.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.visual_0
Keywords: speech

Visualizing microvascular flow variation in OCTA using variable interscan time analysis (VISTA) - Moult, Eric M.; Ploner, Stefan; Choi, Woojhon; Lee, Byungkun; Husvogt, Lennart; Lu, Chen D.; Novais, Eduardo A.; Cole, Emily D.; Potsaid, Benjamin M.; Duker, Jay S.; Hornegger, Joachim; Maier, Andreas; et al. (Proc. SPIE 10053, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXI, 2017)
OCT angiography (OCTA) has recently garnered immense interest in clinical ophthalmology, permitting ocular vasculature to be viewed in exquisite detail, in vivo, and without the injection of exogenous dyes. However, commercial OCTA systems provide little information about actual erythrocyte speeds; instead, OCTA is typically used to visualize the presence and/or absence of vasculature. This is an important limitation because in many ocular diseases, including diabetic retinopathy (DR) and age-related macular degeneration (AMD), alterations in blood flow, but not necessarily only the presence or absence of vasculature, are thought to be important in understanding pathogenesis. To address this limitation, we have developed an algorithm, variable interscan time analysis (VISTA), which is capable of resolving different erythrocyte speeds. VISTA works by acquiring >2 repeated B-scans, and then computing multiple OCTA signals corresponding to different effective interscan times. The OCTA signals corresponding to different effective interscan times contain independent information about erythrocyte speed. In this study we provide a theoretical overview of VISTA, and investigate the utility of VISTA in studying blood flow alterations in ocular disease. OCTA-VISTA images of eyes with choroidal neovascularization, geographic atrophy, and diabetic retinopathy are presented.<br
UnivIS-Import:2018-09-06:Pub.2017.tech.IMMD.IMMD5.visual
Keywords: oct; octa; oct angiography; vista; variable interscan time analysis; blood flow

Voice Handicap and Health-Related Quality of Life after Treatment for Small Laryngeal Carcinoma - Killguß, Helen; Gottwald, Frank; Haderlein, Tino; Maier, Andreas; Rosanowski, Frank; Iro, Heinrich; Psychogios, Georgios; Schuster, Maria (Folia Phoniatrica Et Logopaedica, 2011)

Volumetric Instance Detection for Overlapping Shoes in Computed Tomography - Leipert, Martin; Herl, Gabriel; Zabler, Simon; Maier, Andreas (13th Conference on Industrial Computed Tomography, 2024)

Vorwort - Tolxdorff, Thomas; Deserno, Thomas; Maier, Andreas; Handels, Heinz; Maier-Hein, Klaus; Palm, Christoph (Informatik Aktuell, 2020)
CRIS-Team Scopus Importer:2020-04-21

Weakly Supervised Deep Learning-Based Optical Coherence Tomography Angiography - Jiang, Zhe; Huang, Zhiyu; Qiu, Bin; Meng, Xiangxi; You, Yunfei; Liu, Xi; Geng, Mufeng; Liu, Gangjun; Zhou, Chuanqing; Yang, Kun; Maier, Andreas; Ren, Qiushi; et al. (IEEE transactions on medical imaging, 2021)
Optical coherence tomography angiography (OCTA) is a promising imaging modality for microvasculature studies. Deep learning networks have been widely applied in the field of OCTA reconstruction, benefiting from its powerful mapping capability among images. However, these existing deep learning-based methods depend on high-quality labels, which are hard to acquire considering imaging hardware limitations and practical data acquisition conditions. In this article, we proposed an unprecedented weakly supervised deep learning-based pipeline for OCTA reconstruction task, in the absence of high-quality training labels. The proposed pipeline was investigated on an in vivo animal dataset and a human eye dataset by a cross-validation strategy. Compared with supervised learning approaches, the proposed approach demonstrated similar or even better performance in the OCTA reconstruction task. These investigations indicate that the proposed weakly supervised learning strategy is well capable of performing OCTA reconstruction, and has a certain potential towards clinical applications.
CRIS-Team Scopus Importer:2021-02-26
Keywords: noise2noise; OCTA; ophthalmology; vascular imaging; Weakly supervised learning

Weakly Supervised Segmentation of Cracks on Solar Cells using Normalized Lp Norm - Mayr, Martin; Hoffmann, Mathis; Maier, Andreas; Christlein, Vincent (2019 IEEE International Conference on Image Processing (ICIP), 2019)
Photovoltaic is one of the most important renewable energy sources for dealing with world-wide steadily increasing energy consumption. This raises the demand for fast and scalable automatic quality management during production and operation. However, the detection and segmentation of cracks on electroluminescence (EL) images of mono- or polycrystalline solar modules is a challenging task. In this work, we propose a weakly supervised learning strategy that only uses image-level annotations to obtain a method that is capable of segmenting cracks on EL images of solar cells. We use a modified ResNet-50 to derive a segmentation from network activation maps. We use defect classification as a surrogate task to train the network. To this end, we apply normalized Lp normalization to aggregate the activation maps into single scores for classification. In addition, we provide a study how different parameterizations of the normalized Lp layer affect the segmentation performance. This approach shows promising results for the given task. However, we think that the method has the potential to solve other weakly supervised segmentation problems as well.<br
Keywords: crack detection; weakly supervised semantic segmentation; EL imaging; solar cell; normalized Lp norm

WE‐G‐217BCD‐05: Fiducial Marker‐Based Motion Compensation for the Acquisition of 3D Knee Geometry Under Weight‐Bearing Conditions Using a C‐Arm CT Scanner - Choi, J-H; Keil, A.; Maier, Andreas; Pal, S.; Mcwalter, E. J.; Fahrig, R. (Medical Physics, 2012)
Purpose: Imaging the knee under realistic load‐bearing conditions can be carried out in a horizontal plane using a C‐arm CT scanner. Human subjects can be scanned in a standing position and acquired data successfully reconstructed. However, reconstructing this data is a challenge due to significant artifacts that are induced due to involuntary motion. Here, we propose motion correction methods in 2D and 3D. Methods: Four volunteers were scanned for 8 seconds while squatting with ∼30 degree flexion. Eight tantalum fiducial markers suitably attached around the knee were used to track motion. The marker position in each projection was semi‐ automatically detected. Each marker's static 3D position, which served as a reference to correct temporal motion, was estimated by triangulating each marker's 2D position from 248 projections using known projection matrices. Motion was corrected in 3 ways: 1) 2D projection shifting based on the mean position of markers, 2) 2D projection warping using approximate thin‐ plate splines, 3) 3D rigid body warping. Results: The original reconstruction was severely motion‐corrupted which made it impossible to distinguish the boundaries of bones. Reconstruction with projection shifting and warping in 2D improved visualization of edges of soft tissue as well as bone. A simple numerical metric of residual bead deviation from static position was reduced from 3.2mm to 0.4mm. The 2D‐based methods are inherently limited in that they cannot fully accommodate different 3D movements at different depths from the X‐ray source. Reconstruction with 3D warping shows clearer edges and less streak artifact than the 2D methods. Conclusions: The proposed three motion correction methods effectively reduced motion‐induced artifacts in the reconstruction and are therefore suitable for weight‐bearing scanning. Future work includes scanning patients in standing position after contrast injection for evaluating the soft tissue structure and constructing 3D finite element models for the estimation of joint cartilage stress. This study was supported by Center for Biomedical Imaging at Stanford, by Siemens AG, Healthcare Sector, and by the Lucas Foundation at Stanford. The concepts and contents proposed here are based on research and are not commercially available. © 2012, American Association of Physicists in Medicine. All rights reserved.
CRIS-Team Scopus Importer:2022-08-05

Weight-Bearing C-Arm Cone-Beam CT Using a Dynamic Range Reducer (DyRaR): Initial Prototype and Phantom Studies - Bennett, N.; Zhang, H.; Mueller, K.; Fahrig, R.; Maier, Andreas; Levenston, M.; Gold, G.; Wang, A. (MEDICAL PHYSICS, 2021)
CRIS-Team WoS Importer:2021-09-03

Whole-Body Multi-Organ Segmentation Using Anatomical Attention - Liu, Chang; Denzinger, Felix; Folle, Lukas; Qiu, Jingna; Klein, Laura; Maier, Joscha; Kachelrieb, Marc; Lell, Micheal; Maier, Andreas (Proceedings - International Symposium on Biomedical Imaging, 2023)
Automated multi-organ segmentation is an increasingly important field of research enabling a holistic assessment of the human body. Deep learning-based methods, which are increasingly potent at segmentation tasks, struggle to provide anatomy consistent results due to the highly diverse input data. This e.g. may lead to the prediction of the liver in the head region as the data-driven method is not aware of typical locations of these organs. In this work, we propose to use anatomical attention to circumvent this problem. We define it as distance maps with respect to anatomical landmarks. We also introduce a pipeline to enable the use of anatomical attention. In a first step, anatomical distance maps are extracted using a lightweight attention network. These attention distance maps are then fused with the image data to provide additional anatomical information for a segmentation network. In total, the proposed pipeline is leveraged for a whole-body multi-organ segmentation task of 14 organs. In our evaluation, conducted as an ablation study, the added anatomical attention leads to an average dice score of 60.2, compared to 53.2 of an attention U-Net and 41.8 of a vanilla U-Net. Furthermore, we achieved anatomically plausible predictions.
CRIS-Team Scopus Importer:2023-10-06
Keywords: Attention; Multi-organ; Segmentation; Whole-body

Will We Ever Have Conscious Machines? - Krauß, Patrick; Maier, Andreas (Frontiers in Computational Neuroscience, 2020)
The question of whether artificial beings or machines could become self-aware or conscious has been a philosophical question for centuries. The main problem is that self-awareness cannot be observed from an outside perspective and the distinction of being really self-aware or merely a clever imitation cannot be answered without access to knowledge about the mechanism's inner workings. We investigate common machine learning approaches with respect to their potential ability to become self-aware. We realize that many important algorithmic steps toward machines with a core consciousness have already been taken.
CRIS-Team WoS Importer:2021-01-22

With Semantics and Hidden Markov Models to an Adaptive Log File Parser - Kuhnert, Nadine; Maier, Andreas (International Journal on Natural Language Computing, 2020)
We aim to model an adaptive log file parser. As the content of log files often evolves over time, we established a dynamic statistical model which learns and adapts processing and parsing rules. First, we limit the amount of unstructured text clustering based on semantics of log file lines. Next, we only take the most relevant cluster into account and focus only on those frequent patterns which lead to the desired output table similar to Vaarandi [10]. Furthermore, we transform the found frequent patterns and the output stating the parsed table into a Hidden Markov Model (HMM). We use this HMM as a specific, however, flexible representation of a pattern for log file parsing. With changes in the raw log file distorting learned patterns, we aim the model to adapt automatically in order to maintain high quality output. After training our model on one system type and applying the resulting, adaptive parsing rules to a different system with slightly different log file patterns, we achieve an accuracy over 99.99%.<br
Keywords: Hidden Markov Models; Parameter Extraction; Parsing; Text Mining; Information Retrieval

Workflow Phase Detection in Fluoroscopic Images Using Convolutional Neural Networks - Arbogast, Nikolaus; Kurzendorfer, Tanja; Breininger, Katharina; Mountney, Peter; Toth, Daniel; Narayan, Srinivas A.; Maier, Andreas (Informatik aktuell, 2019)
In image guided interventions, the radiation dose to the patient and personnel can be reduced by positioning the blades of a collimator to block off unnecessary X-rays and restrict the irradiated area to a region of interest. In a certain stage of the operation workflow phase detection can define objects of interest to enable automatic collimation. Workflow phase detection can be beneficial for clinical time management or operating rooms of the future. In this work, we propose a learningbased approach for an automatic classification of three surgical workflow phases. Our data consists of 24 congenital cardiac interventions with a total of 2985 fluoroscopic 2D X-ray images. We compare two different convolutional neural network architectures and investigate their performance regarding each phase. Using a residual network, a class-wise averaged accuracy of 86.14% was achieved. The predictions of the trained models can then be used for context specific collimation.
CRIS-Team Scopus Importer:2019-05-14

Writer Identification Using GMM Supervectors and Exemplar-SVMs - Christlein, Vincent; Bernecker, David; Hönig, Florian Thomas; Maier, Andreas; Angelopoulou, Elli (Pattern Recognition, 2017)
UnivIS-Import:2017-01-09:Pub.2017.tech.IMMD.IMMD5.writer
Keywords: Writer identification; GMM supervectors; Exemplar-SVM

XDose: toward online cross-validation of experimental and computational X-ray dose estimation - Roser, Philipp; Birkhold, Annette; Preuhs, Alexander; Ochs, Philipp; Stepina, Elizaveta; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2020)
Purpose: As the spectrum of X-ray procedures has increased both for diagnostic and for interventional cases, more attention is paid to X-ray dose management. While the medical benefit to the patient outweighs the risk of radiation injuries in almost all cases, reproducible studies on organ dose values help to plan preventive measures helping both patient as well as staff. Dose studies are either carried out retrospectively, experimentally using anthropomorphic phantoms, or computationally. When performed experimentally, it is helpful to combine them with simulations validating the measurements. In this paper, we show how such a dose simulation method, carried out together with actual X-ray experiments, can be realized to obtain reliable organ dose values efficiently. Methods: A Monte Carlo simulation technique was developed combining down-sampling and super-resolution techniques for accelerated processing accompanying X-ray dose measurements. The target volume is down-sampled using the statistical mode first. The estimated dose distribution is then up-sampled using guided filtering and the high-resolution target volume as guidance image. Second, we present a comparison of dose estimates calculated with our Monte Carlo code experimentally obtained values for an anthropomorphic phantom using metal oxide semiconductor field effect transistor dosimeters. Results: We reconstructed high-resolution dose distributions from coarse ones (down-sampling factor 2 to 16) with error rates ranging from 1.62 % to 4.91 %. Using down-sampled target volumes further reduced the computation time by 30 % to 60 %. Comparison of measured results to simulated dose values demonstrated high agreement with an average percentage error of under 10 % for all measurement points. Conclusions: Our results indicate that Monte Carlo methods can be accelerated hardware-independently and still yield reliable results. This facilitates empirical dose studies that make use of online Monte Carlo simulations to easily cross-validate dose estimates on-site.
CRIS-Team Scopus Importer:2020-12-11
Keywords: Anthropomorphic phantom; Dosimetry; Monte Carlo simulation; MOSFET

X-ray Computed Tomography of Flame Structure in Porous Media Burners - Wu, Meng; Dunnmon, Jared; Xia, Yan; Hinshaw, Waldo; Pelc, Norbert; Maier, Andreas; Fahrig, Rebecca; Ihme, Matthias (Proceedings of CT Meeting 2016, 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.xrayco

X-ray CT Data Completeness Condition for Sets of Arbitrary Projections - Herl, Gabriel; Maier, Andreas; Zabler, Simon (Proceedings of SPIE - The International Society for Optical Engineering, 2022)
X-ray tomography reconstruction requires a set of projections that provides sufficient information for the examined region. Commonly, to ensure mathematically complete reconstruction, first, a continuous curve (trajectory) that fulfils the Tuy conditions is chosen. Second, this curve is sampled based on the Nyquist-Shannon sampling theorem. This two-step approach is efficient for most standard X-ray tomography scanning scenarios. For agile X-ray tomography systems, e.g. robot-supported computed tomography systems, choosing a set of projections based on a continuous curve is often not useful. Instead, sets of projections from arbitrary views might be necessary. This work combines the Tuy-Smith condition with conclusions from the Nyquist-Shannon sampling theorem. In particular, the maximal pixel size and a requirement for the arrangement of projections are formulated depending on the smallest relevant object feature and integrated into the Tuy-Smith condition. We derive a comprehensive condition for data completeness that can assess the completeness of any set of arbitrary projections, e.g. for complex scanning scenarios with robot-supported X-ray tomography systems.
CRIS-Team Scopus Importer:2022-11-25
Keywords: arbitrary scan geometry; Data completeness condition; Nyquist-Shannon sampling theorem; robot-supported CT; Tuy conditions

X-ray Imaging - Berger, Martin; Yang, Qiao; Maier, Andreas (Springer, 2018)
UnivIS-Import:2019-03-21:Pub.2018.tech.IMMD.IMMD5.xrayim

X-ray Scatter Estimation Using Deep Splines - Roser, Philipp; Birkhold, Annette; Preuhs, Alexander; Syben-Leisner, Christopher; Felsner, Lina; Hoppe, Elisabeth; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (IEEE Transactions on Medical Imaging, 2021)
X-ray scatter compensation is a very desirable technique in flat-panel X-ray imaging and cone-beam computed tomography. State-of-the-art U-net based scatter removal approaches yielded promising results. However, as there are no physics’ constraints applied to the output of the U-Net, it cannot be ruled out that it yields spurious results. Unfortunately, in the context of medical imaging, those may be misleading and could lead to wrong conclusions. To overcome this problem, we propose to embed B-splines as a known operator into neural networks. This inherently constrains their predictions to well-behaved and smooth functions. In a study using synthetic head and thorax data as well as real thorax phantom data, we found that our approach performed on par with U-net when comparing both algorithms based on quantitative performance metrics. However, our approach not only reduces runtime and parameter complexity, but we also found it much more robust to unseen noise levels. While the U-net responded with visible artifacts, the proposed approach preserved the X-ray signal’s frequency characteristics.
CRIS-Team Scopus Importer:2021-05-07
Keywords: Approximation; B-spline; Detectors; Estimation; Neural network; Neural networks; Photonics; Splines (mathematics); Thorax; X-ray imaging; X-ray scatter

X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery - Bier, Bastian; Unberath, Mathias; Zaech, Jan-Nico; Fotouhi, Javad; Armand, Mehran; Osgood, Greg; Navab, Nassir; Maier, Andreas (MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT IV, 2018)
CRIS-Team WoS Importer:2019-08-09