Teacher-Student Semi-supervised Approach for Medical Image Segmentation

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

Abstract

Accurate segmentation of anatomical structures is a critical step for medical image analysis. Deep learning architectures have become the state-of-the-art models for automatic medical image segmentation. However, these models require an extensive labelled dataset to achieve a high performance. Given that obtaining annotated medical datasets is very expensive, in this work we present a two-phase teacher-student approach for semi-supervised learning. In phase 1, a three network U-Net ensemble, denominated the teacher, is trained using the labelled dataset. In phase 2, a student U-Net network is trained with the labelled dataset and the unlabelled dataset with pseudo-labels produced with the teacher network. The student network is then used for inference of the testing images. The proposed approach is evaluated on the task of abdominal segmentation from the FLARE2022 challenge, achieving a mean 0.53 dice, 0.57 NSD, and 44.97 prediction time on the validation set.

Original languageEnglish
Title of host publicationFast and Low-Resource Semi-supervised Abdominal Organ Segmentation - MICCAI 2022 Challenge, FLARE 2022, Held in Conjunction with MICCAI 2022, Proceedings
EditorsJun Ma, Bo Wang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages152-162
Number of pages11
ISBN (Print)9783031239106
DOIs
StatePublished - 2022
EventInternational challenge on Fast and Lowresource Semi-supervised Abdominal Organ Segmentation in CT Scans, FLARE 2022 held in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: 22 Sep 202222 Sep 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13816 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational challenge on Fast and Lowresource Semi-supervised Abdominal Organ Segmentation in CT Scans, FLARE 2022 held in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
Country/TerritorySingapore
CitySingapore
Period22/09/2222/09/22

Keywords

  • Image segmentation
  • Medical image analysis
  • Semi-supervised learning

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