Medical Knowledge Constrained Semantic Breast Ultrasound Image Segmentation

Kuan Huang, H. D. Cheng, Yingtao Zhang, Boyu Zhang, Ping Xing, Chunping Ning

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

38 Scopus citations

Abstract

Computer-aided diagnosis (CAD) can help doctors in diagnosing breast cancer. Breast ultrasound (BUS) imaging is harmless, effective, portable, and is the most popular modality for breast cancer detection/diagnosis. Many researchers work on improving the performance of CAD systems. However, there are two main shortcomings: (1) Most of the existing methods are based on prerequisites that there is one and only one tumor in the image. (2) The results depend on the datasets, i.e., an algorithm using different datasets may obtain different performances. It implies that the performance of traditional methods is dataset-dependent. In this paper, we propose an effective approach: (1) using information extended images to train a fully convolutional network (FCN) to semantically segment BUS image into 3 categories: Mammary layer, tumor, and background; and (2) applying layer structure information - the breast cancers are located inside the mammary layer - to the conditional random field (CRF) for conducting breast cancer segmentation and making the segmentation result more accurate. The proposed method is evaluated utilizing BUS images of 325 cases, and the result is the best comparing with that of the existing methods by achieving true positive rate 92.80%, false positive rate 9%, and Intersection over Union 82.11%. The proposed approach has solved the above mentioned two shortcomings of the existing methods.

Original languageEnglish
Title of host publication2018 24th International Conference on Pattern Recognition, ICPR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1193-1198
Number of pages6
ISBN (Electronic)9781538637883
DOIs
StatePublished - 26 Nov 2018
Event24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China
Duration: 20 Aug 201824 Aug 2018

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2018-August
ISSN (Print)1051-4651

Conference

Conference24th International Conference on Pattern Recognition, ICPR 2018
Country/TerritoryChina
CityBeijing
Period20/08/1824/08/18

Keywords

  • breast ultrasound (BUS) image
  • computer aided diagnosis (CAD)
  • conditional random field (CRF)
  • deep convolutional neural network (DCNN)
  • semantic image segmentation

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