Liver segmentation from multimodal images using HED-mask R-CNN

Supriti Mulay, G. Deepika, S. Jeevakala, Keerthi Ram, Mohanasankar Sivaprakasam

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

Abstract

Precise segmentation of the liver is critical for computer-aided diagnosis such as pre-evaluation of the liver for living donor-based transplantation surgery. This task is challenging due to the weak boundaries of organs, countless anatomical variations, and the complexity of the background. Computed tomography (CT) scanning and magnetic resonance imaging (MRI) images have different parameters and settings. Thus, images acquired from different modalities differ from one another making liver segmentation challenging task. We propose an efficient liver segmentation with the combination of holistically-nested edge detection (HED) and Mask- region-convolutional neural network (R-CNN) to address these challenges. The proposed HED-Mask R-CNN approach is based on effective identification of edge map from multimodal images. The proposed system firstly applies a preprocessing step of image enhancement to get the ‘primal sketches’ of the abdomen. Then the HED network is applied to enhanced CT and MRI modality images to get better edge map. Finally, the Mask R-CNN is used to segment the liver from edge map images. We used a dataset of 20 CT patients and 9 MR patient from the CHAOS challenge. The system is trained on CT and MRI images separately and then converted to 2D slices. We significantly improved the segmentation accuracy of CT and MRI images on a database with Dice value of 0.94 for CT, 0.89 for T2-weighted MRI and 0.91 for T1-weighted MRI.

Original languageEnglish
Title of host publicationMultiscale Multimodal Medical Imaging - 1st International Workshop, MMMI 2019, held in Conjunction with MICCAI 2019, Proceedings
EditorsQuanzheng Li, Xiang Li, Richard Leahy, Bin Dong
PublisherSpringer Paris
Pages68-75
Number of pages8
ISBN (Print)9783030379681
DOIs
Publication statusPublished - 01-01-2020
Externally publishedYes
Event1st International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 13-10-201913-10-2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11977 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period13-10-1913-10-19

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Mulay, S., Deepika, G., Jeevakala, S., Ram, K., & Sivaprakasam, M. (2020). Liver segmentation from multimodal images using HED-mask R-CNN. In Q. Li, X. Li, R. Leahy, & B. Dong (Eds.), Multiscale Multimodal Medical Imaging - 1st International Workshop, MMMI 2019, held in Conjunction with MICCAI 2019, Proceedings (pp. 68-75). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11977 LNCS). Springer Paris. https://doi.org/10.1007/978-3-030-37969-8_9