Liver Tumor Segmentation in CT Scans Using Modified SegNet

Abstract

The main cause of death related to cancer worldwide is from hepatic cancer. Detection of hepatic cancer early using computed tomography (CT) could prevent millions of patients’ death every year. However, reading hundreds or even tens of those CT scans is an enormous burden for radiologists. Therefore, there is an immediate need is to read, detect, and evaluate CT scans automatically, quickly, and accurately. However, liver segmentation and extraction from the CT scans is a bottleneck for any system, and is still a challenging problem. In this work, a deep learning-based technique that was proposed for semantic pixel-wise classification of road scenes is adopted and modified to fit liver CT segmentation and classification. The architecture of the deep convolutional encoder–decoder is named SegNet, and consists of a hierarchical correspondence of encode–decoder layers. The proposed architecture was tested on a standard dataset for liver CT scans and achieved tumor accuracy of up to 99.9% in the training phase.

A Multiple Layer U-Net, Un-Net, for Liver and Liver Tumor Segmentation in CT

“…In the second stage, we used the best weight from liver segmentation model to train the tumor segmentation model. The cascade structure was implemented in our system, this architecture was used in many studies such as [16], [18], [20], [40], and [41]. The advantage of the cascade structure is to reduce the cases of false positives.…”

“…Several methods of automatic segmentation of liver and lesion have been proposed, consisting of level set parameter [8], [9], fast fuzzy c-means and adaptive watershed [10], [11], fully convolutional networks (FCNs) [12]- [15], segnet [16], encoder-decoder structure [17]- [23]. The most popular encoder-decoder architecture is the U-Net model [24] that has been modified to implement a lot of applications on medical image segmentation such as ischemic stroke lesion [25], pancreas [26], [27], retina vessel [28], [29], prostate [30], colorectal tumor [31], and brain tumor [32], etc.…”

Liver Tumor Segmentation using Superpixel based Fast Fuzzy C Means Clustering

“…Majority of image segmentation algorithms based on improved FCM clustering require more execution time and incapable to give desired results because of two reasons [28]. The primary reason is that the immoderate computational complexity within a local neighboring window is due to the repeated distance calculation between clustering centers and pixels.…”

Dense Dilated Inception Network for Medical Image Segmentation

“…Accurate and automated segmentation of anatomical structures is the most critical and challenging task in analyzing medical images. Medical image segmentation extracts the region of interest for the diagnosis and treatment of various diseases [1], including brain cancer [2], cardiovascular diseases [3], liver cancer [4], pulmonary disease [5], etc., and the list goes on. Accurate and automatic segmentation of anatomical structures is the most important and demanding activity of medical imaging Medical image analysis aims to provide radiologists and clinicians with an efficient, accurate, and precise interpretation of medical images, reducing the time, cost, and error for effective diagnosis.…”