Teacher-Student Semi-supervised Approach for Medical Image Segmentation

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Resumen

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.

Idioma originalInglés
Título de la publicación alojadaFast and Low-Resource Semi-supervised Abdominal Organ Segmentation - MICCAI 2022 Challenge, FLARE 2022, Held in Conjunction with MICCAI 2022, Proceedings
EditoresJun Ma, Bo Wang
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas152-162
Número de páginas11
ISBN (versión impresa)9783031239106
DOI
EstadoPublicada - 2022
EventoInternational 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, Singapur
Duración: 22 sep. 202222 sep. 2022

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen13816 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

ConferenciaInternational 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
País/TerritorioSingapur
CiudadSingapore
Período22/09/2222/09/22

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