Siamese U-Net with Healthy Template for Accurate Segmentation of Intracranial Hemorrhage

Doyoung Kwon, Jaesin Ahn, Jaeil Kim, Inchul Choi, Sungmoon Jeong, Young Sup Lee, Jaechan Park, Minho Lee

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

25 Scopus citations

Abstract

Intracranial hemorrhage (ICH) is a fatal form of stroke which is caused by bleeding within or around the brain. Detection and quantification of hemorrhage are critical in the diagnosis and treatment of the disease. In this paper, we propose Siamese U-Net, to segment the abnormal regions of ICH more accurately from patients’ CT images. The Siamese U-Net is given a paired set of the patients’ CT images and a healthy template of the brain CT. We introduce the dissimilarity of hemorrhage regions from the healthy template to the long skip-connection in the U-Net architecture to emphasize the convolutional features of the abnormal regions by ICH. We evaluate the accuracy of the proposed architecture with a comparison of the baseline model. The proposed model shows significant improvement in Hausdorff distance (6.81%), dice score (9.07%), and volume percentage error (40.32%), compared to the baseline U-Net model. Regarding the healthy template, less both false-negative and false-positive regions are observed in the results of the Siamese U-Net. Consequently, the estimated blood volume by the Siamese U-Net is much closer to the actual volume than that of the baseline U-Net.

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
Pages848-855
Number of pages8
ISBN (Print)9783030322472
DOIs
StatePublished - 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)
Volume11766 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

  • Brain
  • Convolutional neural network
  • Intracranial hemorrhage
  • Non-contrast CT
  • Siamese U-Net
  • Template

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