AIMC Topic: Liver

Clear Filters Showing 391 to 400 of 641 articles

A 3D Deep Neural Network for Liver Volumetry in 3T Contrast-Enhanced MRI.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin
PURPOSE:  To create a fully automated, reliable, and fast segmentation tool for Gd-EOB-DTPA-enhanced MRI scans using deep learning.

Deep learning segmentation of general interventional tools in two-dimensional ultrasound images.

Medical physics
PURPOSE: Many interventional procedures require the precise placement of needles or therapy applicators (tools) to correctly achieve planned targets for optimal diagnosis or treatment of cancer, typically leveraging the temporal resolution of ultraso...

Multi-to-binary network (MTBNet) for automated multi-organ segmentation on multi-sequence abdominal MRI images.

Physics in medicine and biology
Fully convolutional neural network (FCN) has achieved great success in semantic segmentation. However, the performance of the FCN is generally compromised for multi-object segmentation. Multi-organ segmentation is very common while challenging in the...

Histomorphological investigation of intrahepatic connective tissue for surgical anatomy based on modern computer imaging analysis.

Journal of hepato-biliary-pancreatic sciences
BACKGROUND/PURPOSE: Computer-assisted tissue imaging and analytical techniques were used to clarify the histomorphological structure of hepatic connective tissue as a practical guide for surgeons.

Deep-learning-based accurate hepatic steatosis quantification for histological assessment of liver biopsies.

Laboratory investigation; a journal of technical methods and pathology
Hepatic steatosis droplet quantification with histology biopsies has high clinical significance for risk stratification and management of patients with fatty liver diseases and in the decision to use donor livers for transplantation. However, patholo...

Enhanced Integrated Gradients: improving interpretability of deep learning models using splicing codes as a case study.

Genome biology
Despite the success and fast adaptation of deep learning models in biomedical domains, their lack of interpretability remains an issue. Here, we introduce Enhanced Integrated Gradients (EIG), a method to identify significant features associated with ...

Assessing the Robustness of Frequency-Domain Ultrasound Beamforming Using Deep Neural Networks.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
We study training deep neural network (DNN) frequency-domain beamformers using simulated and phantom anechoic cysts and compare to training with simulated point target responses. Using simulation, physical phantom, and in vivo scans, we find that tra...