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ECR 2019 / C-2866
Cloud-based semi-automated liver segmentation: analytical study to compare its speed and accuracy with a semi-automated workstation based software
Congress: ECR 2019
Poster No.: C-2866
Type: Scientific Exhibit
Keywords: Artificial Intelligence, CT, Segmentation
Authors: V. Venugopal1, A. Chunduru2, M. Barnwal3, D. S. Mahra4, A. Raj2, K. Vaidhya2, A. Rangasai Devalla2, V. Mahajan3, H. Mahajan 3; 1Aligarh/IN, 2Bangalore/IN, 3New Delhi/IN, 4Bengaluru, Karnataka/IN

Aims and objectives

Living Donor Liver Transplantation (LDLT) is the commonest form of liver transplantation in Asia. While Deceasead Donor Liver Transplantation (DDLT) constitutes more than 90% of liver transplantation in the western world, in India and many other Asian countries, the majority of transplants performed are LDLT.


Living Donor Liver Transplants is now a well established procedure and has reduced liver transplant waiting list mortality. The complexities of the procedure, along with the donor risks, are the biggest obstacles to widespread use of this valuable treatment option. Pre-operative surgical evaluation and preparation is perhaps the single most important determinant of successful outcomes for both donors and recipients.

For that reason, all LDLT preoperative studies are designed to provide the most accurate information about anatomy, volume and function of the graft and remnant donor liver. These data are integrated with recipient clinical information to determine the optimal surgical strategy.


Preoperative analysis primarily involves segmentation of liver and associated structures to calculate their volumes. Recently, Deep Neural Networks have shown to obtain outstanding performances in many computer vision tasks including image classification[1], object detection[2], object segmentation[3], instance segmentation[4]. They have also been used for medical image segmentation tasks including liver[5], brain tumour[6], multiple sclerosis[7], ischemic stroke[8], etc.


The purpose of this study is to evaluate the performance of a fully automated post processing solution, based on deep neural networks, for liver on MDCT image datasets. We compare time taken to perform liver volumetry between (1) Manual segmentation using commercially available software and (2) Automated segmentation with manual refinement.

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