Automatic liver segmentation in computed tomography scans using deep semantic segmentation

Kadry Ali Ezzat, Lamia Nabil Omran, Ahmed Ibrahim Bahgat El Seddawy

Abstract


Division of the liver from figured computed tomography (CT) images is fundamental for the greater part of the PC supported clinical applications, for instance, the arranging period of a liver transfer, liver volume assessment, and radiotherapy. In this paper, a programmed liver location model from clinical CT filters utilizing profound semantic division convolutional neural organization will be introduced, this model will actually want to subsequently isolate the liver utilizing CT images. The proposed model presents simultaneously the liver ID and the probabilistic division utilizing a profound convolutional neural organization. The proposed approach was endorsed on 10 CT volumes taken from open data sets 3Dircadb1. The proposed model is totally programmed with no requirement for client mediation. Quantitative results show that proposed model is reliable and exact for hepatic volume assessment in a clinical course of action with testing exactness 98.8%.

Keywords


Computed tomography images; Convolution neural network; Deep learning; Liver; Segmentation

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DOI: https://doi.org/10.11591/eei.v12i1.4022

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