Title: Modified U-Net for fully automatic liver segmentation from abdominal CT-image

Authors: Gajendra Kumar Mourya; Sudip Paul; Akash Handique; Ujjwal Baid; Prasad Vilas Dutande; Sanjay Nilkant Talbar

Addresses: Department of Biomedical Engineering, SOT, North Eastern Hill University, Shilong, Meghalaya, India ' Department of Biomedical Engineering, SOT, North Eastern Hill University, Shilong, Meghalaya, India ' Department of Radiology & Imaging, North Eastern Indira Gandhi Regional Institute of Health & Medical Sciences, Shilong, Meghalaya, India ' Center of Excellence in Signal and Image Processing, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, Maharashtra, India ' Center of Excellence in Signal and Image Processing, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, Maharashtra, India ' Center of Excellence in Signal and Image Processing, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, Maharashtra, India

Abstract: Liver volume estimation using segmentation is the first step for liver diagnosis and its therapeutic planning. Liver segmentation from an abdominal CT image has always been a universal challenge for researchers because of low contrast among surrounding organs. An automatic liver segmentation technique is extremely desired in clinical practice. In this paper, we have modified conventional U-Net architecture for automatic liver segmentation. This method will precisely delineate the boundaries between the liver and other abdominal organs and outperforms over another state of the art methods. We extensively evaluated our method on 'CHAOS challenge-2019 dataset of 20 subjects' volumetric CT images. Quantitative evaluation of the proposed method is done in terms of various evaluation parameters with respect to their ground truth. Result achieved average dice similarity coefficient 0.97±0.03 and precision 0.93±0.12. In conclusion, the obtained results from this work demonstrated substantially significant performance with consistency and robustness.

Keywords: computed tomography; liver segmentation; U-Net; semantic segmentation; deep learning.

DOI: 10.1504/IJBET.2022.125099

International Journal of Biomedical Engineering and Technology, 2022 Vol.40 No.1, pp.1 - 17

Received: 21 May 2019
Accepted: 31 Oct 2019
Published online: 30 Aug 2022 *

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