The liver is a frequent site of benign and malignant, primary and metastatic tumors. Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are the most common primary liver cancers, and colorectal liver metastasis (CRLM) is the most common secondary liver cancer. Although the imaging characteristic of these tumors is central to optimal clinical management, it relies on imaging features that are often non-specific, overlap, and are subject to inter-observer variability. Thus, in this study, we aimed to categorize liver tumors automatically from CT scans using a deep learning approach that objectively extracts discriminating features not visible to the naked eye. Specifically, we used a modified Inception v3 network-based classification model to classify HCC, ICC, CRLM, and benign tumors from pretreatment portal venous phase computed tomography (CT) scans. Using a multi-institutional dataset of 814 patients, this method achieved an overall accuracy rate of 96%, with sensitivity rates of 96%, 94%, 99%, and 86% for HCC, ICC, CRLM, and benign tumors, respectively, using an independent dataset. These results demonstrate the feasibility of the proposed computer-assisted system as a novel non-invasive diagnostic tool to classify the most common liver tumors objectively.

This paper provides an alternative approach to the co-operative multi-robot path planning problem using parallel differential evolution algorithms. Both centralized and distributed realizations for multi-robot path planning have been studied, and the performances of the methods have been compared with respect to a few pre-defined yardsticks. The distributed approach to this problem out-performs its centralized version for multi-robot planning. Relative performance of the distributed version of the differential evolution algorithm has been studied with varying numbers of robots and obstacles. The distributed version of the algorithm is also compared with a PSO-based realization, and the results are competitive.

In the current era of machine learning and radiomics, one of the challenges is the automatic segmentation of organs and tumors. Tumor detection is mostly based on a radiologist’s manual reading, which necessitates a high level of professional abilities and clinical experience. Moreover, increasing the high volume of images makes radiologists’ assessments more challenging. Artificial intelligence (AI) can assist clinicians in diagnosing cancer at an early stage by providing a solution for assisted medical image analysis. The automated segmentation of tumor is better realized through conventional segmentation methods and, nowadays, through machine learning and deep learning techniques. The segmentation of abdominal organs and tumors from various imaging modalities has gained much attention in recent years. Among these, pancreas and pancreatic tumor are the most challenging to segment and have recently drawn a lot of attraction. The main objective of this paper is to give a summary of different automated approaches for the segmentation of pancreas and pancreatic tumors and to perform a comparative analysis using various indices such as dice similarity coefficient (DSC), sensitivity (SI), specificity (SP), precision (Pr), recall and Jaccard index (JI), etc. Finally, the limitations and future research perspectives of pancreas and tumor segmentation are summarized.