Volume 11, Number 5
Fatima Abdalbagi1, Serestina Viriri2 and Mohammed Tajalsir Mohammed1, 1Sudan University of Science and Technology, Sudan, 2University of KwaZulu-Natal, South Africa
In computer vision, image segmentation is defined as process of a partition of an image in a number of regions with homogeneous features. The region of our interest here is the liver. Prior to the deep learning revolution traditional handcrafted features were used for liver segmentation but with deep learning the features are obtained automatically. There are many semiautomatic and fully automatic approaches have been proposed to improve the liver segmentation procedure some of them use deep learning techniques for Segmentation and other one use a Classical Based method for Segmentation. In this paper we aim to enhance our previous work which we were proposed a Batch Normalization After All - Convolutional Neural Network (BATA-Convnet) model to segment the liver, where the Dice is equal to 0.91% when implement our BATA Convnet using MICCA dataset and Dice is equal to 0.84% when implement it using 3D-IRCAD dataset. Here in this paper we propose BATA-Unet model for liver segmentation, it's based on Unet architecture as backbone but differ in we added a batch-normalization layer an after each convolution layer in both construction path and expanding path. The proposed method was able to achieve highest dice similarity coefficient than the previous work where for MICCA dataset Dice =0.97% and for 3D-IRCAD dataset =0.96%. Also our proposed model outperformed other state-of-the-art model when we compare it with them.
Liver segmentation, Deep learning, Computed Tomography, Handcrafted, U-NET.