RBC Semantic Segmentation for Sickle Cell Disease Based on Deformable U-Net

Mo Zhang, Xiang Li, Mengjia Xu, Quanzheng Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations

Abstract

Reliable cell segmentation and classification from biomedical images is a crucial step for both scientific research and clinical practice. A major challenge for more robust segmentation and classification methods is the large variations in the size, shape and viewpoint of the cells, combining with the low image quality caused by noise and artifacts. To address this issue, in this work we propose a learning-based, simultaneous cell segmentation and classification method based on the U-Net structure with deformable convolution layers. The U-Net architecture has been shown to offer a precise localization for image semantic segmentation. Moreover, deformable convolution enables the free form deformation of the feature learning process, thus making the whole network more robust to various cell morphologies and image settings. The proposed method is tested on microscopic red blood cell images from patients with sickle cell disease. The results show that U-Net with deformable convolution achieves the highest accuracy for both segmentation and classification tasks, compared with the original U-Net structure and unsupervised methods.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
EditorsAlejandro F. Frangi, Gabor Fichtinger, Julia A. Schnabel, Carlos Alberola-López, Christos Davatzikos
PublisherSpringer Verlag
Pages695-702
Number of pages8
ISBN (Print)9783030009366
DOIs
StatePublished - 2018
Externally publishedYes
Event21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: Sep 16 2018Sep 20 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11073 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Country/TerritorySpain
CityGranada
Period9/16/189/20/18

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

Keywords

  • Deformable convolution
  • RBC semantic segmentation
  • Sickle cell disease
  • U-Net

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