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High-level prior-based loss functions for medical image segmentation : a survey

Rosana El Jurdi 1, 2 Caroline Petitjean 1 Paul Honeine 1 Veronika Cheplygina 3 Fahed Abdallah 2, 4 
1 DocApp - LITIS - Equipe Apprentissage
LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
4 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
LIST3N - Laboratoire Informatique et Société Numérique
Abstract : Today, deep convolutional neural networks (CNNs) have demonstrated state of the art performance for supervised medical image segmentation, across various imaging modalities and tasks. Despite early success, segmentation networks may still generate anatomically aberrant segmentations, with holes or inaccuracies near the object boundaries. To mitigate this effect, recent research works have focused on incorporating spatial information or prior knowledge to enforce anatomically plausible segmentation. If the integration of prior knowledge in image segmentation is not a new topic in classical optimization approaches, it is today an increasing trend in CNN based image segmentation, as shown by the growing literature on the topic. In this survey, we focus on high level prior, embedded at the loss function level. We categorize the articles according to the nature of the prior: the object shape, size, topology, and the inter-regions constraints. We highlight strengths and limitations of current approaches, discuss the challenge related to the design and the integration of prior-based losses, and the optimization strategies, and draw future research directions.
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https://hal-normandie-univ.archives-ouvertes.fr/hal-03410506
Contributor : Paul Honeine Connect in order to contact the contributor
Submitted on : Sunday, October 31, 2021 - 11:09:44 PM
Last modification on : Tuesday, July 12, 2022 - 3:50:01 PM

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Rosana El Jurdi, Caroline Petitjean, Paul Honeine, Veronika Cheplygina, Fahed Abdallah. High-level prior-based loss functions for medical image segmentation : a survey. Computer Vision and Image Understanding, Elsevier, 2021, 210, pp.103248. ⟨10.1016/j.cviu.2021.103248⟩. ⟨hal-03410506⟩

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