Segmentation of liver tumors plays an important role in the choice of therapeutic strategies for liver disease and treatment monitoring. In this paper, we generalize the process of a level set with a novel algorithm of dynamic regulation to energy fu...
Journal of cellular and molecular medicine
Feb 19, 2019
Hepatocellular carcinoma (HCC) is closely associated with abnormal DNA methylation. In this study, we analyzed 450K methylation chip data from 377 HCC samples and 50 adjacent normal samples in the TCGA database. We screened 47,099 differentially meth...
RATIONALE AND OBJECTIVES: To use machine learning-based magnetic resonance imaging radiomics to predict metachronous liver metastases (MLM) in patients with rectal cancer.
International journal of medical informatics
Jan 7, 2019
AIMS: To develop a natural language processing (NLP)-based algorithm for extracting clinically useful information for patients with hepatocellular carcinoma (HCC) from Chinese electronic medical records (EMRs) and use these data for the assessment of...
Searching for new leads in the battle of cancer will never ends, we herein disclose the design and synthesis of new phthalazine derivatives and their in vitro and in vivo testing for their antiproliferative activity. Phthalazine was selected as a pri...
BACKGROUND: N6-methyladensine (m6A) is a common and abundant RNA methylation modification found in various species. As a type of post-transcriptional methylation, m6A plays an important role in diverse RNA activities such as alternative splicing, an ...
Liver tumor segmentation from computed tomography (CT) images is a critical and challenging task. Due to the fuzziness in the liver pixel range, the neighboring organs of the liver with the same intensity, high noise and large variance of tumors. The...
IEEE journal of biomedical and health informatics
Dec 11, 2018
Deep learning (DL) architectures have opened new horizons in medical image analysis attaining unprecedented performance in tasks such as tissue classification and segmentation as well as prediction of several clinical outcomes. In this paper, we prop...
Purpose To develop and evaluate a fully automated algorithm for segmenting the abdomen from CT to quantify body composition. Materials and Methods For this retrospective study, a convolutional neural network based on the U-Net architecture was traine...