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Pancreas

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DLLabelsCT: Annotation tool using deep transfer learning to assist in creating new datasets from abdominal computed tomography scans, case study: Pancreas.

PloS one
The utilization of artificial intelligence (AI) is expanding significantly within medical research and, to some extent, in clinical practice. Deep learning (DL) applications, which use large convolutional neural networks (CNN), hold considerable pote...

Detection of Peri-Pancreatic Edema using Deep Learning and Radiomics Techniques.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Pancreatitis is a major public health issue world-wide; studies show an increase in the number of people experiencing pancreatitis. Identifying peri-pancreatic edema is a pivotal indicator for identifying disease progression and prognosis, emphasizin...

The potential of AI-assisted gastrectomy with dual highlighting of pancreas and connective tissue.

Surgical oncology
BACKGROUND: Standard gastrectomy with D2 lymph node (LN) dissection for gastric cancer involves peripancreatic lymphadenectomy [1]. This technically demanding procedure requires meticulous dissection within the dissectable layers of connective tissue...

Deep learning reconstruction for accelerated high-resolution upper abdominal MRI improves lesion detection without time penalty.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to compare a conventional T1-weighted volumetric interpolated breath-hold examination (VIBE) sequence with a DL-reconstructed accelerated high-resolution VIBE sequence (HR-VIBE) in terms of image quality, lesion...

Large-scale multi-center CT and MRI segmentation of pancreas with deep learning.

Medical image analysis
Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, la...

Artificial Intelligence in Pancreatic Imaging: A Systematic Review.

United European gastroenterology journal
The rising incidence of pancreatic diseases, including acute and chronic pancreatitis and various pancreatic neoplasms, poses a significant global health challenge. Pancreatic ductal adenocarcinoma (PDAC) for example, has a high mortality rate due to...

Extracting organs of interest from medical images based on convolutional neural network with auxiliary and refined constraints.

Scientific reports
Accurately extracting organs from medical images provides radiologist with more comprehensive evidences to clinical diagnose, which offers up a higher accuracy and efficiency. However, the key to achieving accurate segmentation lies in abundant clues...

[Pancreas segmentation with multi-channel convolution and combined deep supervision].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Due to its irregular shape and varying contour, pancreas segmentation is a recognized challenge in medical image segmentation. Convolutional neural network (CNN) and Transformer-based networks perform well but have limitations: CNN have constrained r...

Facing the challenges of autoimmune pancreatitis diagnosis: The answer from artificial intelligence.

World journal of gastroenterology
Current diagnosis of autoimmune pancreatitis (AIP) is challenging and often requires combining multiple dimensions. There is a need to explore new methods for diagnosing AIP. The development of artificial intelligence (AI) is evident, and it is belie...

RRM-TransUNet: Deep-Learning Driven Interactive Model for Precise Pancreas Segmentation in CT Images.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Pancreatic diseases such as cancer and pancreatitis pose significant health risks. Early detection requires precise segmentation results. Fully automatic segmentation algorithms cannot integrate clinical expertise and correct output error...