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Liver Neoplasms

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[Robot-Assisted Right Hemihepatectomy for Hepatocellular Carcinoma].

Zentralblatt fur Chirurgie
Since the introduction of robot-assisted surgery, increasingly complex operations have been performed with this technique. Robot-assisted operations are also of increasing importance in hepatobiliary surgery. With articulated and scaled movements in ...

Prediction of Microvascular Invasion of Hepatocellular Carcinoma Based on Preoperative Diffusion-Weighted MR Using Deep Learning.

Academic radiology
RATIONALE AND OBJECTIVES: To investigate the value of diffusion-weighted magnetic resonance imaging for the prediction of microvascular invasion (MVI) of Hepatocellular Carcinoma (HCC) using Convolutional Neural Networks (CNN).

Deep learning-based radiomics predicts response to chemotherapy in colorectal liver metastases.

Medical physics
PURPOSE: The purpose of this study was to develop and validate a deep learning (DL)-based radiomics model to predict the response to chemotherapy in colorectal liver metastases (CRLM).

Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network.

IEEE transactions on medical imaging
Fully convolutional neural networks have made promising progress in joint liver and liver tumor segmentation. Instead of following the debates over 2D versus 3D networks (for example, pursuing the balance between large-scale 2D pretraining and 3D con...

Spatial feature fusion convolutional network for liver and liver tumor segmentation from CT images.

Medical physics
PURPOSE: The accurate segmentation of liver and liver tumors from CT images can assist radiologists in decision-making and treatment planning. The contours of liver and liver tumors are currently obtained by manual labeling, which is time-consuming a...

Toward reliable automatic liver and tumor segmentation using convolutional neural network based on 2.5D models.

International journal of computer assisted radiology and surgery
PURPOSE: We investigated the parameter configuration in the automatic liver and tumor segmentation using a convolutional neural network based on 2.5D model. The implementation of 2.5D model shows promising results since it allows the network to have ...

Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers.

Gut
Artificial intelligence (AI) can extract complex information from visual data. Histopathology images of gastrointestinal (GI) and liver cancer contain a very high amount of information which human observers can only partially make sense of. Complemen...

NucleiSegNet: Robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images.

Computers in biology and medicine
The nuclei segmentation of hematoxylin and eosin (H&E) stained histopathology images is an important prerequisite in designing a computer-aided diagnostics (CAD) system for cancer diagnosis and prognosis. Automated nuclei segmentation methods enable ...

Robotic hepatic resection in postero-superior region of liver.

Updates in surgery
OBJECTIVE: Laparoscopic hepatectomy in the posterosuperior hepatic region is technically challenging and demanding. However, minimally invasive procedures carried out using the Da Vinci robot provide potential advantages in complex hepatectomy. This ...

PAIP 2019: Liver cancer segmentation challenge.

Medical image analysis
Pathology Artificial Intelligence Platform (PAIP) is a free research platform in support of pathological artificial intelligence (AI). The main goal of the platform is to construct a high-quality pathology learning data set that will allow greater ac...