blob loss: Instance Imbalance Aware Loss Functions for Semantic Segmentation

Florian Kofler*, Suprosanna Shit, Ivan Ezhov, Lucas Fidon, Izabela Horvath, Rami Al-Maskari, Hongwei Bran Li, Harsharan Bhatia, Timo Loehr, Marie Piraud, Ali Erturk, Jan Kirschke, Jan C. Peeken, Tom Vercauteren, Claus Zimmer, Benedikt Wiestler, Bjoern Menze

*Corresponding author for this work

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

5 Citations (Scopus)

Abstract

Deep convolutional neural networks (CNN) have proven to be remarkably effective in semantic segmentation tasks. Most popular loss functions were introduced targeting improved volumetric scores, such as the Dice coefficient (DSC). By design, DSC can tackle class imbalance, however, it does not recognize instance imbalance within a class. As a result, a large foreground instance can dominate minor instances and still produce a satisfactory DSC. Nevertheless, detecting tiny instances is crucial for many applications, such as disease monitoring. For example, it is imperative to locate and surveil small-scale lesions in the follow-up of multiple sclerosis patients. We propose a novel family of loss functions, blob loss, primarily aimed at maximizing instance-level detection metrics, such as F1 score and sensitivity. Blob loss is designed for semantic segmentation problems where detecting multiple instances matters. We extensively evaluate a DSC-based blob loss in five complex 3D semantic segmentation tasks featuring pronounced instance heterogeneity in terms of texture and morphology. Compared to soft Dice loss, we achieve 5% improvement for MS lesions, 3% improvement for liver tumor, and an average 2% improvement for microscopy segmentation tasks considering F1 score.

Original languageEnglish
Title of host publicationInformation Processing in Medical Imaging - 28th International Conference, IPMI 2023, Proceedings
EditorsAlejandro Frangi, Marleen de Bruijne, Demian Wassermann, Nassir Navab
PublisherSpringer Science and Business Media Deutschland GmbH
Pages755-767
Number of pages13
ISBN (Print)9783031340475
DOIs
Publication statusPublished - 2023
Event28th International Conference on Information Processing in Medical Imaging, IPMI 2023 - San Carlos de Bariloche, Argentina
Duration: 18 Jun 202323 Jun 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13939 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference28th International Conference on Information Processing in Medical Imaging, IPMI 2023
Country/TerritoryArgentina
CitySan Carlos de Bariloche
Period18/06/202323/06/2023

Keywords

  • instance imbalance awareness
  • lightsheet microscopy
  • multiple sclerosis
  • semantic segmentation loss function

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