Share Email Print
cover

Proceedings Paper • new

Towards increased trustworthiness of deep learning segmentation methods on cardiac MRI
Author(s): Jörg Sander; Bob D. de Vos; Jelmer M. Wolterink; Ivana Išgum
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Current state-of-the-art deep learning segmentation methods have not yet made a broad entrance into the clinical setting in spite of high demand for such automatic methods. One important reason is the lack of reliability caused by models that fail unnoticed and often locally produce anatomically implausible results that medical experts would not make. This paper presents an automatic image segmentation method based on (Bayesian) dilated convolutional networks (DCNN) that generate segmentation masks and spatial uncertainty maps for the input image at hand. The method was trained and evaluated using segmentation of the left ventricle (LV) cavity, right ventricle (RV) endocardium and myocardium (Myo) at end-diastole (ED) and end-systole (ES) in 100 cardiac 2D MR scans from the MICCAI 2017 Challenge (ACDC). Combining segmentations and uncertainty maps and employing a human-in-the-loop setting, we provide evidence that image areas indicated as highly uncertain, regarding the obtained segmentation, almost entirely cover regions of incorrect segmentations. The fused information can be harnessed to increase segmentation performance. Our results reveal that we can obtain valuable spatial uncertainty maps with low computational effort using DCNNs.

Paper Details

Date Published: 15 March 2019
PDF: 7 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094919 (15 March 2019); doi: 10.1117/12.2511699
Show Author Affiliations
Jörg Sander, Univ. Medical Ctr. Utrecht (Netherlands)
Bob D. de Vos, Univ. Medical Ctr. Utrecht (Netherlands)
Jelmer M. Wolterink, Univ. Medical Ctr. Utrecht (Netherlands)
Ivana Išgum, Univ. Medical Ctr. Utrecht (Netherlands)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

© SPIE. Terms of Use
Back to Top