Abstract: Robust Multi-Scale Anatomical Landmark Detection in Incomplete 3D-CT Data - Ghesu, Florin-Cristian; Georgescu, Bogdan; Grbic, Sasa; Maier, Andreas; Hornegger, Joachim; Comaniciu, Dorin (Informatik Aktuell, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.abstra_0

Artificial Intelligence for Medical Image Understanding - Ghesu, Florin-Cristian (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2019)
Robust and fast detection and segmentation of anatomical structures in medical image data represents an important component of medical image analysis technologies. Current solutions for this problem are nowadays based on machine learning techniques that exploit large annotated image databases in order to learn the appearance of the captured anatomy. These solutions are subject to several limitations. This includes the use of suboptimal image feature engineering methods and most importantly the use of computationally suboptimal search-schemes for anatomy parsing, e.g., exhaustive hypotheses scanning. In particular, these techniques do not effectively address cases of incomplete data, i.e., scans acquired with a partial field-of-view. To address these challenges, we introduce in this thesis marginal space deep learning, a framework for medical image parsing which exploits the automated feature design of deep learning models and an efficient object parametrization scheme in hierarchical marginal spaces. To support the efficient evaluation of solution hypotheses under complex transformations, such as rotation and anisotropic scaling, we propose a novel cascaded network architecture, called sparse adaptive neural network. Experiments on detecting and segmenting the aortic root in 2891 3D ultrasound volumes from 869 patients, demonstrate a high level of robustness with an accuracy increase of 30-50% against the state-of-the-art. Despite these facts, using a scanning routine to explore large parameter subspaces results in a high computational complexity, with false-positive predictions and limited scalability to high resolution volumetric data. To deal with these limitations, we propose a novel paradigm for medical image parsing, based on principles of cognitive modeling and behavior learning. The anatomy detection problem is reformulated as a behavior learning task for an intelligent artificial agent. Using deep reinforcement learning, agents are taught how to search for an anatomical structure. This resumes to learning to navigate optimal search trajectories through the image space that converge to the locations of the sought anatomical structures. To support the effective parsing of high resolution volumetric data, we apply elements from scale-space theory and enhance our framework to support the learning of multi-scale search strategies through the scale-space representation of medical images. Finally, to enable the accurate recognition whether certain anatomical landmarks are missing from the field-of-view, we exploit prior knowledge about the anatomy and ensure the spatial coherence of the agents by using statistical shape modeling and robust estimation theory. Comprehensive experiments demonstrate a high level of accuracy, compared to state-of-the-art solutions, without failures of clinical significance. In particular, our method achieves 0% false positive and 0% false negative rates at detecting whether anatomical structures are captured in the field-of-view (excluding border cases). The dataset contains 5043 3D computed tomography volumes from over 2000 patients, totaling over 2,500,000 image slices. A significant increase in accuracy compared to reference solutions is achieved on additional 2D ultrasound and 2D/3D magnetic resonance datasets, containing up to 1000 images. Most importantly, this paradigm improves the detection-speed of the previous solutions by 2-3 orders of magnitude, achieving unmatched real-time performance on high resolution volumetric scans.<br
Keywords: Artificial Intelligence; Deep Learning; Medical Image Analysis; Medical Image Parsing; Reinforcement Learning

Deep Learning Computed Tomography - Würfl, Tobias; Ghesu, Florin-Cristian; Christlein, Vincent; Maier, Andreas (Medical Image Computing and Computer-Assisted Intervention MICCAI 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.deeple

Guided Image Super-Resolution: A New Technique for Photogeometric Super-Resolution in Hybrid 3-D Range Imaging - Ghesu, Florin-Cristian; Köhler, Thomas; Haase, Sven; Hornegger, Joachim (Pattern Recognition, 2014)
In this paper, we augment multi-frame super-resolution with the concept of guided filtering for simultaneous upsampling of 3-D range data and complementary photometric information in hybrid range imaging. Our guided super-resolution algorithm is formulated as joint maximum a-posteriori estimation to reconstruct high-resolution range and photometric data. In order to exploit local correlations between both modalities, guided filtering is employed for regularization of the proposed joint energy function. For fast and robust image reconstruction, we employ iteratively re-weighted least square minimization embedded into a cyclic coordinate descent scheme. The proposed method was evaluated on synthetic datasets and real range data acquired with Microsoft’s Kinect. Our experimental evaluation demonstrates that our approach outperforms state-of-the-art range super-resolution algorithms while it also provides super-resolved photometric data.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.guided_65

Multi-task Learning for Chest X-ray Abnormality Classification on Noisy Labels - Gündel, Sebastian; Ghesu, Florin-Cristian; Grbic, Sasa; Gibson, Eli; Georgescu, Bogdan; Maier, Andreas; Comaniciu, Dorin (2019)

Noise Reduction In Low-Dose CT Using a 3D Multiscale Sparse Denoising Autoencoder - Mentl, Katrin; Mailhé, Boris; Ghesu, Florin-Cristian; Schebesch, Frank; Haderlein, Tino; Maier, Andreas; Nadar, Mariappan (2017 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.noiser_1

Pectoral Muscle Detection in Digital Breast Tomosynthesis and Mammography - Ghesu, Florin-Cristian; Wels, Michael; Jerebko, Anna; Sühling, Michael; Hornegger, Joachim; Kelm, B. Michael (Medical Computer Vision. Large Data in Medical Imaging, 2014)
Screening and diagnosis of breast cancer with Digital Breast Tomosynthesis (DBT) and Mammography are increasingly supported by algorithms for automatic post-processing. The pectoral muscle, which dorsally delineates the breast tissue towards the chest wall, is an important anatomical structure for navigation. Along with the nipple and the skin, the pectoral muscle boundary is often used for reporting the location of breast lesions. It is visible in mediolateral oblique (MLO) views where it is well approximated by a straight line. Here, we propose two machine learning-based algorithms to robustly detect the pectoral muscle in MLO views from DBT and mammography. Embedded into the Marginal Space Learning framework, the algorithms involve the evaluation of multiple candidate boundaries in a hierarchical manner. To this end, we propose a novel method for candidate generation using a Hough-based approach. Experiments were performed on a set of 100 DBT volumes and 95 mammograms from different clinical cases. Our novel combined approach achieves competitive accuracy and robustness. In particular, for the DBT data, we achieve significantly lower deviation angle error and mean distance error than the standard approach. The proposed algorithms run within a few seconds. © 2014 Springer International Publishing Switzerland.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.pector

Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment - Ghesu, Florin-Cristian; Georgescu, Bogdan; Gibson, Eli; Gündel, Sebastian; Kalra, Mannudeep; Singh, Ramandeep; Digumarthy, Subba; Grbic, Sasa; Comaniciu, Dorin (Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, 2019)
The interpretation of chest radiographs is an essential task for the detection of thoracic diseases and abnormalities. However, it is a challenging problem with high inter-rater variability and inherent ambiguity due to inconclusive evidence in the data, limited data quality or subjective definitions of disease appearance. Current deep learning solutions for chest radiograph abnormality classification are typically limited to providing probabilistic predictions, relying on the capacity of learning models to adapt to the high degree of label noise and become robust to the enumerated causal factors. In practice, however, this leads to overconfident systems with poor generalization on unseen data. To account for this, we propose an automatic system that learns not only the probabilistic estimate on the presence of an abnormality, but also an explicit uncertainty measure which captures the confidence of the system in the predicted output. We argue that explicitly learning the classification uncertainty as an orthogonal measure to the predicted output, is essential to account for the inherent variability characteristic of this data. Experiments were conducted on two datasets of chest radiographs of over 85,000 patients. Sample rejection based on the predicted uncertainty can significantly improve the ROC-AUC, e.g., by 8% to 0.91 with an expected rejection rate of under 25%. Eliminating training samples using uncertainty-driven bootstrapping, enables a significant increase in robustness and accuracy. In addition, we present a multi-reader study showing that the predictive uncertainty is indicative of reader errors.<br

Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment - Gündel, Sebastian; Setio, Arnaud A.A.; Ghesu, Florin-Cristian; Grbic, Sasa; Georgescu, Bogdan; Maier, Andreas; Comaniciu, Dorin (Medical Image Analysis, 2021)
Chest radiography is the most common radiographic examination performed in daily clinical practice for the detection of various heart and lung abnormalities. The large amount of data to be read and reported, with more than 100 studies per day for a single radiologist, poses a challenge in consistently maintaining high interpretation accuracy. The introduction of large-scale public datasets has led to a series of novel systems for automated abnormality classification. However, the labels of these datasets were obtained using natural language processed medical reports, yielding a large degree of label noise that can impact the performance. In this study, we propose novel training strategies that handle label noise from such suboptimal data. Prior label probabilities were measured on a subset of training data re-read by 4 board-certified radiologists and were used during training to increase the robustness of the training model to the label noise. Furthermore, we exploit the high comorbidity of abnormalities observed in chest radiography and incorporate this information to further reduce the impact of label noise. Additionally, anatomical knowledge is incorporated by training the system to predict lung and heart segmentation, as well as spatial knowledge labels. To deal with multiple datasets and images derived from various scanners that apply different post-processing techniques, we introduce a novel image normalization strategy. Experiments were performed on an extensive collection of 297,541 chest radiographs from 86,876 patients, leading to a state-of-the-art performance level for 17 abnormalities from 2 datasets. With an average AUC score of 0.880 across all abnormalities, our proposed training strategies can be used to significantly improve performance scores.
Keywords: label noise; robust loss function; multi-task learning; chest radiography abnormality classification

Robust Multi-Scale Anatomical Landmark Detection in Incomplete 3D-CT Data - Ghesu, Florin-Cristian; Georgescu, Bogdan; Grbic, Sasa; Maier, Andreas; Hornegger, Joachim; Comaniciu, Dorin (Medical Image Computing and Computer-Assisted Intervention MICCAI 2017, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.robust_1

Robust Non-Rigid Registration through Agent-Based Action Learning - Krebs, Julian; Mansi, Tommaso; Delingette, Herve; Zhang, Li; Ghesu, Florin-Cristian; Miao, Shun; Maier, Andreas; Ayache, Nicholas; Liao, Rui; Kamen, Ali (Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.robust_0

Towards Intelligent Robust Detection of Anatomical Structures in Incomplete Volumetric Data - Ghesu, Florin-Cristian; Georgescu, Bogdan; Grbic, Sasa; Maier, Andreas; Hornegger, Joachim; Comaniciu, Dorin (Medical Image Analysis, 2018)
Robust and fast detection of anatomical structures represents an important component of medical image analysis technologies. Current solutions for anatomy detection are based on machine learning, and are generally driven by suboptimal and exhaustive search strategies. In particular, these techniques do not effectively address cases of incomplete data, i.e., scans acquired with a partial field-of-view. We address these challenges by following a new paradigm, which reformulates the detection task to teaching an intelligent artificial agent how to actively search for an anatomical structure. Using the principles of deep reinforcement learning with multi-scale image analysis, artificial agents are taught optimal navigation paths in the scale-space representation of an image, while accounting for structures that are missing from the field-of-view. The spatial coherence of the observed anatomical landmarks is ensured using elements from statistical shape modeling and robust estimation theory. Experiments show that our solution outperforms marginal space deep learning, a powerful deep learning method, at detecting different anatomical structures without any failure. The dataset contains 5043 3D-CT volumes from over 2000 patients, totaling over 2,500,000 image slices. In particular, our solution achieves 0% false-positive and 0% false-negative rates at detecting whether the landmarks are captured in the field-of-view of the scan (excluding all border cases), with an average detection accuracy of 2.78 mm. In terms of runtime, we reduce the detection-time of the marginal space deep learning method by 20–30 times to under 40 ms, an unmatched performance for high resolution incomplete 3D-CT data.
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.toward_1
Keywords: Deep learning; Deep reinforcement learning; Incomplete 3D-data; M-estimator sample consensus; Multi-scale detection; Real-time detection; Robust statistical shape-modeling; Scale-space modeling

Tutorial: Deep Learning - Advancing the State-of-the-Art in Medical Image Analysis - Christlein, Vincent; Ghesu, Florin-Cristian; Würfl, Tobias; Maier, Andreas; Isensee, Fabian; Neher, Peter; Maier-Hein, Klaus (Bildverarbeitung für die Medizin 2017, 2017)
UnivIS-Import:2018-09-06:Pub.2017.tech.IMMD.IMMD5.tutori