DeepSplit: Segmentation of Microscopy Images Using Multi-task Convolutional Networks

Andrew Torr, Doga Basaran, Julia Sero, Jens Rittscher, Heba Sailem

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

1 Citation (SciVal)


Accurate segmentation of cellular structures is critical for automating the analysis of microscopy data. Advances in deep learning have facilitated extensive improvements in semantic image segmentation. In particular, U-Net, a model specifically developed for biomedical image data, performs multi-instance segmentation through pixel-based classification. However, approaches based on U-Net tend to merge touching cells in dense cell cultures, resulting in under-segmentation. To address this issue, we propose DeepSplit; a multi-task convolutional neural network architecture where one encoding path splits into two decoding branches. DeepSplit first learns segmentation masks, then explicitly learns the more challenging cell-cell contact regions. We test our approach on a challenging dataset of cells that are highly variable in terms of shape and intensity. DeepSplit achieves 90% cell detection coefficient and 90% Dice Similarity Coefficient (DSC) which is a significant improvement on the state-of-the-art U-Net that scored 70% and 84% respectively.

Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis - 24th Annual Conference, MIUA 2020, Proceedings
EditorsBartlomiej W. Papiez, Ana I.L. Namburete, Mohammad Yaqub, J. Alison Noble, Mohammad Yaqub
Place of PublicationCham, Switzerland
Number of pages13
ISBN (Print)9783030527907
Publication statusE-pub ahead of print - 8 Jul 2020
Event24th Annual Conference on Medical Image Understanding and Analysis, MIUA 2020 - Oxford, UK United Kingdom
Duration: 15 Jul 202017 Jul 2020

Publication series

NameCommunications in Computer and Information Science
Volume1248 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference24th Annual Conference on Medical Image Understanding and Analysis, MIUA 2020
Country/TerritoryUK United Kingdom

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)


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