Machine learning based liver disease diagnosis: A systematic review
The computer-based approach is required for the non-invasive detection of chronic liver
diseases that are asymptomatic, progressive, and potentially fatal in nature. In this study, we …
diseases that are asymptomatic, progressive, and potentially fatal in nature. In this study, we …
Automatic 3D liver location and segmentation via convolutional neural network and graph cut
Purpose Segmentation of the liver from abdominal computed tomography (CT) images is an
essential step in some computer-assisted clinical interventions, such as surgery planning for …
essential step in some computer-assisted clinical interventions, such as surgery planning for …
[HTML][HTML] A deep learning approach for liver and tumor segmentation in CT images using ResUNet
According to the most recent estimates from global cancer statistics for 2020, liver cancer is
the ninth most common cancer in women. Segmenting the liver is difficult, and segmenting …
the ninth most common cancer in women. Segmenting the liver is difficult, and segmenting …
RMS-UNet: Residual multi-scale UNet for liver and lesion segmentation
Precise segmentation is in demand for hepatocellular carcinoma or metastasis clinical
diagnosis due to the heterogeneous appearance and diverse anatomy of the liver on …
diagnosis due to the heterogeneous appearance and diverse anatomy of the liver on …
Automatic liver segmentation based on shape constraints and deformable graph cut in CT images
Liver segmentation is still a challenging task in medical image processing area due to the
complexity of the liver's anatomy, low contrast with adjacent organs, and presence of …
complexity of the liver's anatomy, low contrast with adjacent organs, and presence of …
Review of liver segmentation and computer assisted detection/diagnosis methods in computed tomography
Computed tomography (CT) imaging remains the most utilized modality for liver-related
cancer screening and treatment monitoring purposes. Liver, liver tumor and liver vasculature …
cancer screening and treatment monitoring purposes. Liver, liver tumor and liver vasculature …
A two-stage approach for automatic liver segmentation with Faster R-CNN and DeepLab
W Tang, D Zou, S Yang, J Shi, J Dan… - Neural Computing and …, 2020 - Springer
Proper liver segmentation is a key step in many clinical applications, including computer-
assisted diagnosis, radiation therapy and volume measurement. However, liver …
assisted diagnosis, radiation therapy and volume measurement. However, liver …
Deep sequential segmentation of organs in volumetric medical scans
Segmentation in 3-D scans is playing an increasingly important role in current clinical
practice supporting diagnosis, tissue quantification, or treatment planning. The current 3-D …
practice supporting diagnosis, tissue quantification, or treatment planning. The current 3-D …
Survey on liver tumour resection planning system: steps, techniques, and parameters
Preoperative planning for liver surgical treatments is an essential planning tool that aids in
reducing the risks of surgical resection. Based on the computed tomography (CT) images …
reducing the risks of surgical resection. Based on the computed tomography (CT) images …
Liver tissue segmentation in multiphase CT scans using cascaded convolutional neural networks
Purpose We address the automatic segmentation of healthy and cancerous liver tissues
(parenchyma, active and necrotic parts of hepatocellular carcinoma (HCC) tumor) on …
(parenchyma, active and necrotic parts of hepatocellular carcinoma (HCC) tumor) on …