Automated detection and segmentation of mediastinal and axillary lymph nodes from CT using foveal fully convolutional networks

Heike Carolus; Andra-Iza Iuga; Tom Brosch; Rafael Wiemker; Frank Thiele; Anna Höink; David Maintz; Michael Püsken; Tobias Klinder

doi:10.1117/12.2549246

16 March 2020 Automated detection and segmentation of mediastinal and axillary lymph nodes from CT using foveal fully convolutional networks

Heike Carolus, Andra-Iza Iuga, Tom Brosch, Rafael Wiemker, Frank Thiele, Anna Höink, David Maintz, Michael Püsken, Tobias Klinder

Author Affiliations +

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113141B (2020) https://doi.org/10.1117/12.2549246
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

The assessment of lymph nodes in CT examinations of cancer patients is essential for cancer staging with direct impact on therapeutic decisions. Automated detection and segmentation of lymph nodes is challenging, especially, due to significant variability in size, shape and location coupled with weak and variable image contrast. In this paper, we propose a joint detection and segmentation approach using a fully convolutional neural network based on 3D foveal patches. To enable network training, 89 publicly available CT data sets were carefully re-annotated yielding an extensive set of 4351 voxel-wise segmentations of thoracic lymph nodes. Based on these annotations, the 3D network was trained to perform per voxel classification. For enlarged potentially malignant lymph nodes, a detection rate of 79% with 8.0 false-positive detections per volume was obtained. A DICE of 0.44 was achieved on average.

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Heike Carolus, Andra-Iza Iuga, Tom Brosch, Rafael Wiemker, Frank Thiele, Anna Höink, David Maintz, Michael Püsken, and Tobias Klinder "Automated detection and segmentation of mediastinal and axillary lymph nodes from CT using foveal fully convolutional networks", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113141B (16 March 2020); https://doi.org/10.1117/12.2549246

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