Abstract
With the huge innovative improvement in all lifestyles, it has been important to build up the clinical fields, remembering the finding for which treatment is done; where the fruitful treatment relies upon the preoperative. Models for the preoperative, for example, planning to understand the complex internal structure of the liver and precisely localize the liver surface and its tumors; there are various algorithms proposed to do the automatic liver segmentation. In this paper, we propose a Batch Normalization After All - Convolutional Neural Network (BATA-Convnet) model to segment the liver CT images using Deep Learning Technique. The proposed liver segmentation model consists of four main steps: pre-processing, training the BATA-Convnet, liver segmentation, and the postprocessing step to maximize the result efficiency. Medical Image Computing and Computer Assisted Intervention (MICCAI) dataset and 3DImage Reconstruction for Comparison of Algorithm Database (3D-IRCAD) were used in the experimentation and the average results using MICCAI are 0.91% for Dice, 13.44% for VOE, 0.23% for RVD, 0.29mm for ASD, 1.35mm for RMSSD and 0.36mm for MaxASD. The average results using 3DIRCAD dataset are 0.84% for Dice, 13.24% for VOE, 0.16% for RVD, 0.32mm for ASD, 1.17mm for RMSSD and 0.33mm for MaxASD.
Highlights
The liver segmentation process done manually by trained clinicians but it is time consuming and requiring much effort and it different from one clinician to another because of the observer variability; as the result of that, an automatic liver segmentation system would be a great boon for perform these tasks
The datasets used in this work are the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2007 grand challenge dataset and 3D Image Reconstruction for Comparison of Algorithm Database (3D-IRCAD)
The BATA-Convnet model for liver segmentation using 3D computed tomography (CT) scans is proposed in this paper
Summary
The liver segmentation process done manually by trained clinicians but it is time consuming and requiring much effort and it different from one clinician to another because of the observer variability; as the result of that, an automatic liver segmentation system would be a great boon for perform these tasks. Because of complexity of liver shapes and variable liver sizes among patients the segmentation of the liver from medical images is very difficult and due to low contrast between the liver and surrounding organs like stomach, pancreas, kidney and muscles. The liver is one of the biggest and an essential organ in the human body. It is molded like a cone and situated in the upper right-hand part of the stomach cavity, underneath the stomach. It is responsible for carrying out some very important functions to keep the body pure of toxins and harmful substances [1]. CAD it is one of the major research topics because it is part of the routine
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