Towards increased trustworthiness of deep learning segmentation methods on cardiac MRI

Jörg Sander*, Bob D. de Vos, Jelmer M. Wolterink, Ivana Išgum

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationMedical Imaging 2019
Subtitle of host publicationImage Processing
EditorsElsa D. Angelini, Bennett A. Landman
Place of PublicationBellingham, WA
PublisherSPIE Press
Number of pages7
ISBN (Electronic)9781510625464
ISBN (Print)9781510625457
Publication statusPublished - 1 Jan 2019
Externally publishedYes
EventSPIE Medical Imaging 2019: Image Processing - San Diego, United States
Duration: 19 Feb 201921 Feb 2019

Publication series

NameProceedings of SPIE
ISSN (Print)1605-7422
ISSN (Electronic)2410-9045


ConferenceSPIE Medical Imaging 2019: Image Processing
CountryUnited States
CitySan Diego


  • Bayesian neural networks
  • Cardiac MRI segmentation
  • Deep learning
  • Loss functions
  • Uncertainty estimation

Fingerprint Dive into the research topics of 'Towards increased trustworthiness of deep learning segmentation methods on cardiac MRI'. Together they form a unique fingerprint.

Cite this