Identifying Thyroid Nodules in Ultrasound Images Through Segmentation-Guided Discriminative Localization

Jintao Lu, Xi Ouyang, Tianjiao Liu, Dinggang Shen

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

Abstract

In this paper, we propose a novel segmentation-guided network for thyroid nodule identification from ultrasound images. Accurate diagnosis of thyroid nodules through ultrasound images is significant for cancer detection at the early stage. Many Computer-Aided Diagnose (CAD) systems for this task ignore the inherent correlation between nodule segmentation task and classification task (i.e. cancer grading). Actually, segmentation results could be used as localization cues of thyroid nodules for facilitating their classifications as benign or malignant. Accordingly, we propose a two-stage thyroid nodule diagnosis method through 1) nodule segmentation and 2) segmentation-guided diagnosis. Specifically, in the segmentation stage, we use an ensemble strategy to integrate segmentations from diverse segmentation networks. Then, in the classification stage, the obtained segmentation result is integrated as additional information along with its corresponding original ultrasound images as the input of the classification network. Meanwhile, the segmentation result is further served as guidance to refine the attention map of the features used for classification. Our method is applied to the TN-SCUI 2020, a MICCAI 2020 Challenge, with the largest set of thyroid nodule ultrasound images according to our knowledge. Our method achieved the 2nd place in its classification challenge.

Original languageEnglish
Title of host publicationSegmentation, Classification, and Registration of Multi-modality Medical Imaging Data - MICCAI 2020 Challenges, ABCs 2020, L2R 2020, TN-SCUI 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsNadya Shusharina, Mattias P. Heinrich, Ruobing Huang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages135-144
Number of pages10
ISBN (Print)9783030718268
DOIs
Publication statusPublished - 2021
Externally publishedYes
EventAnatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images Challenge, ABCs 2020, Learn2Reg Challenge, L2R 2020 and Thyroid Nodule Segmentation and Classification in Ultrasound Images Challenge, TN-SCUI 2020 held in conjunction with 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 2020 Oct 42020 Oct 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12587 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceAnatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images Challenge, ABCs 2020, Learn2Reg Challenge, L2R 2020 and Thyroid Nodule Segmentation and Classification in Ultrasound Images Challenge, TN-SCUI 2020 held in conjunction with 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Country/TerritoryPeru
CityLima
Period20/10/420/10/8

Keywords

  • Attention
  • Identification
  • Thyroid nodule
  • Ultrasound

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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