Performance Analysis of Different 2D and 3D CNN Model for Liver Semantic Segmentation: A Review

Abstract

Image segmentation is one of the most popular methods in automated computational medical image analysis. Precise and significant semantic segmentation on abdominal Magnetic Resonance Imaging (MRI) and Computer Tomography (CT) volume images, specifically liver segmentation has a lot of contribution towards clinical decision making for patient treatment. Apart from the many state-of-the-art methods, different cutting-edge deep learning architectures are being developed rapidly. Those architectures are performing better segmentation while at the same time outperforming other state-of- the-art models. Different deep learning models perform differently based on cell types, organ shapes and the type of medical imaging (i.e. CT, MRI). Starting from 2D convolutional networks (CNN), many variations of 3D convolutional neural network architectures have achieved significant results in segmentation tasks on MRI and CT images. In this paper, we review performance of different 2D and 3D CNN models for liver image segmentation. We also analyzed studies that used variants of ResNet, FCN, U-Net, and 3D U-Net along with various evaluation metrics. How these variants of 2D and 3D CNN models enhance the performance against its state-of-the-art architectures are demonstrated in the results section. Besides the architectural development, each year, new segmentation and other biomedical challenges are being offered. These challenges come with their own datasets. Apart from challenges, some datasets are provided and supported by different organizations. Use of such data set can be found in this study. Moreover, this review of reported results, along with different datasets and architectures will help future researchers in liver semantic segmentation tasks. Furthermore, our listing of results will give the insight to analyze the use of different metrics for the same organs with the change in performances.