Siemens AG: Detection of Complex Stents in Live Fluoroscopic Images for Endovascular Aneurysm Repair
Thesis by: Wei NiAdvisor: Prof. Nassir Navab
Supervision by: Shadi Albarqouni, Stefanie Demirci
Abstract
The abdominal aortic aneurysm (AAA) is a dilation of the aorta that may result in rupture, and is one of the most common aortic diseases. An AAA may be repaired by open surgery or by endovascular aneurysm repair (EVAR) to prevent rupture, and in recent years EVAR has become predominant. During the EVAR procedure, a stent is placed at the position of the aneurysm to exclude it from direct blood flow. The accuracy of stent placement is critical to prevent occlusion of the branching arteries, e.g., renal arteries. This master thesis implements methods to detect complex deployed stents in live 2D fluoroscopic images during EVAR. The proposed learning-based method trains fully convolutional neural network (FCN) models [1, 2, 3] to detect the stent. The training set consists of labelled 2D fluoroscopic image patches that contain the stent. The detection result can be further improved by integrating prior knowledge, e.g., the overlay of the registered pre-operative CT segmentation. Possible evaluation metrics include DICE coefficient (to measure repeatability), true positive rate (sensitivity), positive predictive value (precision) and Hausdorff distance. Previous research [4, 5, 6] has improved the accuracy of stent detection for EVAR. However, the methods are limited to infra-renal, abdominal EVAR cases. In contrast, stents with fenestrations/scallops are necessary for supra-renal cases and for complex AAA anatomy. Methods of this work aim to detect the complexity of the stent. The expected result qualitatively and quantitatively describes which portion/branch of the stent corresponds to which artery. For example, in a supra-renal case, the result describes whether a portion/branch of the stent covers the aorta, the left/right external iliac artery, the left/right renal artery, etc.Location
Siemens AG Advanced Therapies, HC AT IN NGC Siemensstr. 1, 91301 Forchheim, GermanyLiterature
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C. Baur, S. Albarqouni, S. Demirci, N. Navab, P. Fallavollita
CathNets: Detection and Single-View Depth Prediction of Catheter Electrodes 7th International Conference on Medical Imaging and Augmented Reality (MIAR), 24-26 August, 2016, Bern, Switzerland. (Best Paper Award) (bib) |
| D. Volpi, M. H. Sarhan, R. Ghotbi, N. Navab, D. Mateus, S. Demirci
Online Tracking of Interventional Devices for Endovascular Aortic Repair International Journal of Computer Assisted Radiology and Surgery, June 2015, Volume 10, Issue 6, pp 773-781 (bib) |
Resultant Paper
| K. Breininger, S. Albarqouni, T. Kurzendorfer, M. Pfister, M. Kowarschik, A. Maier
Intraoperative Stent Segmentation in X-ray Fluoroscopy for Endovascular Aortic Repair International Journal of Computer Assisted Radiology and Surgery (IJCARS) (bib) |
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| Title: | Siemens AG: Detection of Complex Stents in Live Fluoroscopic Images for Endovascular Aneurysm Repair |
| Abstract: | The abdominal aortic aneurysm (AAA) is a dilation of the aorta that may result in rupture, and is one of the most common aortic diseases. An AAA may be repaired by open surgery or by endovascular aneurysm repair (EVAR) to prevent rupture, and in recent years EVAR has become predominant. During the EVAR procedure, a stent is placed at the position of the aneurysm to exclude it from direct blood flow. The accuracy of stent placement is critical to prevent occlusion of the branching arteries, e.g., renal arteries. This master thesis implements methods to detect complex deployed stents in live 2D fluoroscopic images during EVAR. The proposed learning-based method trains fully convolutional neural network (FCN) models [1, 2, 3] to detect the stent. The training set consists of labelled 2D fluoroscopic image patches that contain the stent. The detection result can be further improved by integrating prior knowledge, e.g., the overlay of the registered pre-operative CT segmentation. Possible evaluation metrics include DICE coefficient (to measure repeatability), true positive rate (sensitivity), positive predictive value (precision) and Hausdorff distance. Previous research [4, 5, 6] has improved the accuracy of stent detection for EVAR. However, the methods are limited to infra-renal, abdominal EVAR cases. In contrast, stents with fenestrations/scallops are necessary for supra-renal cases and for complex AAA anatomy. Methods of this work aim to detect the complexity of the stent. The expected result qualitatively and quantitatively describes which portion/branch of the stent corresponds to which artery. For example, in a supra-renal case, the result describes whether a portion/branch of the stent covers the aorta, the left/right external iliac artery, the left/right renal artery, etc. |
| Student: | Wei Ni |
| Director: | Prof. Dr. Nassir Navab |
| Supervisor: | Shadi Albarqouni, Stefanie Demirci |
| Type: | Master Thesis |
| Area: | Machine Learning, Medical Imaging |
| Status: | finished |
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