AIMC Topic: Gallbladder

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Multi-spatial-attention U-Net: a novel framework for automated gallbladder segmentation on CT images.

BMC medical imaging
OBJECTIVE: This study aimed to construct a novel model, Multi-Spatial Attention U-Net (MSAU-Net) by incorporating our proposed Multi-Spatial Attention (MSA) block into the U-Net for the automated segmentation of the gallbladder on CT images.

GBCHV an advanced deep learning anatomy aware model for accurate classification of gallbladder cancer utilizing ultrasound images.

Scientific reports
This study introduces a novel deep learning approach aimed at accurately classifying Gallbladder Cancer (GBC) into benign, malignant, and normal categories using ultrasound images from the challenging GBC USG (GBCU) dataset. The proposed methodology ...

Machine learning-based prognostic modeling in gallbladder cancer using clinical data and pre-treatment [F]-FDG-PET-radiomic features.

Japanese journal of radiology
OBJECTIVES: This study evaluates the effectiveness of machine learning (ML) models that incorporate clinical and 2-deoxy-2-[F]fluoro-D-glucose ([F]-FDG)-positron emission tomography (PET)-radiomic features for predicting outcomes in gallbladder cance...

Exploring Deep Learning Applications using Ultrasound Single View Cines in Acute Gallbladder Pathologies: Preliminary Results.

Academic radiology
RATIONALE AND OBJECTIVES: In this preliminary study, we aimed to develop a deep learning model using ultrasound single view cines that distinguishes between imaging of normal gallbladder, non-urgent cholelithiasis, and acute calculous cholecystitis r...

The value of CT radiomics combined with deep transfer learning in predicting the nature of gallbladder polypoid lesions.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Computed tomography (CT) radiomics combined with deep transfer learning was used to identify cholesterol and adenomatous gallbladder polyps that have not been well evaluated before surgery.

Multiple instance convolutional neural network for gallbladder assessment from laparoscopic images.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: We present an artificial intelligence framework for vascularity classification of the gallbladder (GB) wall from intraoperative images of laparoscopic cholecystectomy (LC).

Does single-site robotic surgery makes sense for gallbladder surgery?

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Cholecystectomy is one of the most performed surgeries. Several techniques were created, generating less pain, better aesthetic results and faster return to activities. Robotic surgery through a single portal combined the advantages of si...

Ensembled deep learning model outperforms human experts in diagnosing biliary atresia from sonographic gallbladder images.

Nature communications
It is still challenging to make accurate diagnosis of biliary atresia (BA) with sonographic gallbladder images particularly in rural area without relevant expertise. To help diagnose BA based on sonographic gallbladder images, an ensembled deep learn...

Patch-based classification of gallbladder wall vascularity from laparoscopic images using deep learning.

International journal of computer assisted radiology and surgery
PURPOSE: In this study, we propose a deep learning approach for assessment of gallbladder (GB) wall vascularity from images of laparoscopic cholecystectomy (LC). Difficulty in the visualization of GB wall vessels may be the result of fatty infiltrati...

Knowledge-Aided Convolutional Neural Network for Small Organ Segmentation.

IEEE journal of biomedical and health informatics
Accurate and automatic organ segmentation is critical for computer-aided analysis towards clinical decision support and treatment planning. State-of-the-art approaches have achieved remarkable segmentation accuracy on large organs, such as the liver ...