Analysing Variables for 90-Day Functional-Outcome Prediction of Endovascular Thrombectomy - Rodriguez Salas, Dalia; Rieß, Christian; Martín Vicario, Celia; Taubmann, Oliver; Ditt, Hendrik; Schwab, Stefan; Dörfler, Arnd (Medical Image Understanding and Analysis. 28th Annual Conference, MIUA 2024, Manchester, UK, July 24–26, 2024, Proceedings, Part II, 2024)
Multiple studies report the impact of variables on predicting long-term functional outcomes following Endovascular Thrombectomy (EVT). Often, the variables are analyzed separately with univariate statistical analysis or combined in a linear regression or logistic regression prediction. Although such linear combinations are easy to interpret, they do not consider nonlinear relationships. Other studies do consider nonlinearities, most notably in deep learning. However, if any, they use separate methods to assess the variable importance, which might not capture their actual contribution to the prediction. In this work, we propose to use an algorithm that combines the predictive power of neural networks and the interpretation of decision trees in a single method. ForestNet is a special artificial neural network that defines its partially connected architecture based on the hierarchical variable importance given by an ensemble of decision trees, providing a means of interpretation in a more generalized fashion. On predicting 90-day favorable functional outcomes, using 10-fold cross-validation, we reach an AUC of 0.82 and 0.86 before and after EVT, respectively. A 90-day Modified Rankin Scale (mRS)<=2 defines a favorable outcome. The analysis of the variable’s contribution shows that the pre-stroke mRS, age, and the National Institutes of Health Stroke Scale (NIHSS) play the most important role in predictions before EVT. After EVT, the infarct volume from the 24h follow-up CT scan, measured directly or in terms of affected brain regions according to the ASPECT score, is the most important variable in providing a more accurate prediction than before EV
Keywords: Stroke outcome prediction; Interpretable machine learning; Thrombectomy 90-day outcome

A principled representation of elongated structures using heatmaps - Kordon, Florian; Stiglmayr, Michael; Maier, Andreas; Martín Vicario, Celia; Pertlwieser, Tobias; Kunze, Holger (Scientific Reports, 2023)
The detection of elongated structures like lines or edges is an essential component in semantic image analysis. Classical approaches that rely on significant image gradients quickly reach their limits when the structure is context-dependent, amorphous, or not directly visible. This study introduces a principled mathematical description of elongated structures with various origins and shapes. Among others, it serves as an expressive operational description of target functions that can be well approximated by Convolutional Neural Networks. The nominal position of a curve and its positional uncertainty are encoded as a heatmap by convolving the curve distribution with a filter function. We propose a low-error approximation to the expensive numerical integration by evaluating a distance-dependent function, enabling a lightweight implementation with linear time complexity. We analyze the method’s numerical approximation error and behavior for different curve types and signal-to-noise levels. Application to surgical 2D and 3D data, semantic boundary detection, skeletonization, and other related tasks demonstrate the method’s versatility at low errors.<br

Automatic Plane Adjustment in Surgical Cone Beam CT-volumes - Martín Vicario, Celia; Kordon, Florian; Denzinger, Felix; Weiten, Markus; Thomas, Sarina; Kausch, Lisa; Franke, Jochen; Keil, Holger; Maier, Andreas; Kunze, Holger (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Automatic plane adjustment of orthopedic intraoperative flat panel detector CT-volumes - Martín Vicario, Celia; Kordon, Florian; Denzinger, Felix; El Barbari, Jan Siad; Privalov, Maxim; Franke, Jochen; Thomas, Sarina; Kausch, Lisa; Maier, Andreas; Kunze, Holger (Journal of Medical Imaging, 2022)
Purpose: To assess the result in orthopedic trauma surgery, usually three-dimensional volume data of the treated region is acquired. With mobile C-arm systems, these acquisitions can be performed intraoperatively, reducing the number of required revision surgeries. However, the acquired volumes are typically not aligned to the anatomical regions. Thus, the multiplanar reconstructed (MPR) planes need to be adjusted manually during the review of the volume. To speed up and ease the workflow, an automatic parameterization of these planes is needed. Approach: We present a detailed study of multitask learning (MTL) regression networks to estimate the parameters of the MPR planes. First, various mathematical descriptions for rotation, including Euler angle, quaternion, and matrix representation, are revised. Then, two different MTL network architectures based on the PoseNet are compared with a single task learning network. Results: Using a matrix description rather than the Euler angle description, the accuracy of the regressed normals improves from 7.7 deg to 7.3 deg in the mean value for single anatomies. The multihead approach improves the regression of the plane position from 7.4 to 6.1 mm, whereas the orientation does not benefit from this approach. Thus, the achieved accuracy meets the reported interrater variance in similarly complex body regions of up to 6.3 deg for the normals and up to 9.3 mm for the plane position. Conclusions: The use of a multihead approach with shared features leads to more accurate plane regression compared with the use of individual networks for each task. It also improves the angle estimation for the ankle region. The reported results are in the same range as manual plane adjustments. The use of a combined network with shared parameters requires less memory, which is a great benefit for the implementation of an application for the surgical environmen

Automatic Plane Adjustment of Orthopedic Intraoperative Flat Panel Detector CT-Volumes - Martín Vicario, Celia; Kordon, Florian; Denzinger, Felix; Weiten, Markus; Thomas, Sarina; Kausch, Lisa; Franke, Jochen; Keil, Holger; Maier, Andreas; Kunze, Holger (Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 2020)
Flat panel computed tomography is used intraoperatively to assess the result of surgery. Due to workflow issues, the acquisition typically cannot be carried out in such a way that the axis aligned multiplanar reconstructions (MPR) of the volume match the anatomically aligned MPRs. This needs to be performed manually, adding additional effort during viewing the datasets. A PoseNet convolutional neural network (CNN) is trained such that parameters of anatomically aligned MPR planes are regressed. Different mathematical approaches to describe plane rotation are compared, as well as a cost function is optimized to incorporate orientation constraints. The CNN is evaluated on two anatomical regions. For one of these regions, one plane is not orthogonal to the other two planes. The plane’s normal can be estimated with a median accuracy of 5°, the in-plane rotation with an accuracy of 6°, and the position with an accuracy of 6 mm. Compared to state-of-the-art algorithms the labeling effort for this method is much lower as no segmentation is required. The computation time during inference is less than 0.05 s.<br
Keywords: Orthopedics; Flat planel CT; Multiplanar reconstruction

Normalization techniques for CNN based analysis of surgical cone beam CT volumes - Martín Vicario, Celia; Kordon, Florian; Denzinger, Felix; El Barbari, Jan Siad; Privalov, Maxim; Franke, Jochen; Maier, Andreas; Kunze, Holger (Medial Imaging 2022, Image Processing, 2022)
Purpose: The choice of input normalization has effects on the generalization and performance of deep neural networks. While this topic is explored for 2D imaging applications, the influence of different normalization techniques on medical imaging modalities, e.g. cone-beam CT (CBCT), differs due to a different value range and distribution. In this paper a good normalization technique for intra-operatively acquired surgical CBCT volumes is presented. Methods: A set of normalization strategies, namely histogram equalization, min-max scaling, z-score normalization, linear look up table (LUT) with clipping and sigmoid function with clipping is compared on a CBCT volume classification task. Results: The results show that a combination of parameterized LUTs and clipping with the range [-710, 1640] HU independent of the underlying intensity histogram provides the best performance for the task at hand. Conclusions: The clipping based normalization technique helps to compress the feature space to the relevant range. By this approach, most of the information about the intensity values of soft tissue and bone is retained. The clipping range presented in this paper is valid for surgical CBCTs.<br
Keywords: Cone-beam CT; Normalization

Segmentation of the Carotid Lumen and Vessel Wall using Deep Learning and Location Priors - Thamm, Florian; Denzinger, Felix; Rist, Leonhard; Martín Vicario, Celia; Kordon, Florian; Maier, Andreas (2021)
Keywords: Vessel Wall Segmentation; Magnetic Resonance Imaging; Deep Learning

Uncertainty-aware deep learning for trustworthy prediction of long-term outcome after endovascular thrombectomy - Martín Vicario, Celia; Rodriguez Salas, Dalia; Maier, Andreas; Hock, Stefan; Kuramatsu, Joji; Kallmünzer, Bernd; Thamm, Florian; Taubmann, Oliver; Ditt, Hendrik; Schwab, Stefan; Dörfler, Arnd; Mühlen, Iris (Scientific Reports, 2024)
Acute ischemic stroke (AIS) is a leading global cause of mortality and morbidity. Improving long-term outcome predictions after thrombectomy can enhance treatment quality by supporting clinical decision-making. With the advent of interpretable deep learning methods in recent years, it is now possible to develop trustworthy, high-performing prediction models. This study introduces an uncertainty-aware, graph deep learning model that predicts endovascular thrombectomy outcomes using clinical features and imaging biomarkers. The model targets long-term functional outcomes, defined by the three-month modified Rankin Score (mRS), and mortality rates. A sample of 220 AIS patients in the anterior circulation who underwent endovascular thrombectomy (EVT) was included, with 81 (37%) demonstrating good outcomes (mRS≤2). The performance of the different algorithms evaluated was comparable, with the maximum validation under the curve (AUC) reaching 0.87 using graph convolutional networks (GCN) for mRS prediction and 0.86 using fully connected networks (FCN) for mortality prediction. Moderate performance was obtained at admission (AUC of 0.76 using GCN), which improved to 0.84 post-thrombectomy and to 0.89 a day after stroke. Reliable uncertainty prediction of the model could be demonstrated.
CRIS-Team Scopus Importer:2024-05-17