Abdominal Adipose Tissue Segmentation in MRI with Double Loss Function Collaborative Learning

Siyuan Pan, Xuhong Hou, Huating Li, Bin Sheng, Ruogu Fang, Yuxin Xue, Weiping Jia, Jing Qin

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Deep learning has shown promising progress in computer-aided medical image diagnosis in recent years, such adipose tissue segmentation. Generally, training a high-performance deep segmentation model requires a large amount of labeled images. However, in clinical practice many labels are saved in numerical forms rather than image forms while relabelling images with manual segmentation is extremely time-consuming and laborious. To fill in this gap between numerical labels and image-based labels, we propose a novel double loss function to train an adipose segmentation model through collaborative learning. Specifically, the double loss function leverages a large volume of numerical labels available and a small volume of images labels. To validate our collaborative learning model, we collect one dataset of 300 high quality MR images with pixel-level segmentation labels and another dataset of 9000 clinical quantitative MR images with numerical labels of the number of pixels in subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and Non-adipose tissues. Our approach achieves 94.3% and 90.8% segmentation accuracy for SAT and VAT respectively in the dataset with image labels, and 93.6% and 88.7% segmentation accuracy for the dataset with only numerical labels. The proposed approach can be generalize to a broad range of clinical problems with different types of ground truth labels.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
PublisherSpringer Science and Business Media Deutschland GmbH
Pages41-49
Number of pages9
ISBN (Print)9783030322250
DOIs
Publication statusPublished - 2019
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 13 Oct 201917 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11769 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Country/TerritoryChina
CityShenzhen
Period13/10/1917/10/19

Keywords

  • Adipose
  • Deep learning
  • Multi-correlation data
  • Segmentation
  • Weak supervised data

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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