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. 2020 Jun 30;7:105. doi: 10.3389/fcvm.2020.00105

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Copyright © 2020 Chen, Bai, Davies, Bhuva, Manisty, Augusto, Moon, Aung, Lee, Sanghvi, Fung, Paiva, Petersen, Lukaschuk, Piechnik, Neubauer and Rueckert.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Examples of worst segmentation results found on challenging slices. Left: Image, middle: ground truth (GT), right: prediction from the UKBB model. (A) Failure to predict LV when the apical slice has a very small LV. (B) LV segmentation missing on the basal slice (ES frame). This sample is from the BSCMR-AS dataset where only the LV endocardial annotation is available. (C) Failure to recognize the LV due to a stripe of high-intensity noise around the cardiac chambers in this 1.5T image. This sample is an ES frame image from the BSCMR-AS dataset. (D) Failure to estimate the LV structure when unexpected strong dark artifacts disrupt the shape of the LV in this 3T image. Note that this image is an ED frame image from the BSCMR-AS dataset where RV was not annotated by experts.