Share Email Print
cover

Proceedings Paper

Automated detection and segmentation of mediastinal and axillary lymph nodes from CT using foveal fully convolutional networks
Author(s): Heike Carolus; Andra-Iza Iuga; Tom Brosch; Rafael Wiemker; Frank Thiele; Anna Höink; David Maintz; Michael Püsken; Tobias Klinder
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113141B (16 March 2020); doi: 10.1117/12.2549246
Show Author Affiliations
Heike Carolus, Philips Research (Germany)
Andra-Iza Iuga, Univ. zu Köln (Germany)
Tom Brosch, Philips Research (Germany)
Rafael Wiemker, Philips Research (Germany)
Frank Thiele, Philips Healthcare (Germany)
Anna Höink, Univ. zu Köln (Germany)
David Maintz, Univ. zu Köln (Germany)
Michael Püsken, Univ. zu Köln (Germany)
Tobias Klinder, Philips Research (Germany)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray