An optimized two stage U-Net approach for segmentation of pancreas and pancreatic tumor.

The segmentation of pancreas and pancreatic tumor remain a persistent challenge for radiologists. Consequently, it is essential to develop automated segmentation methods to address this task. U-Net based models are most often used among various deep learning-based techniques in tumor segmentation.

Comprehensive segmentation of gray matter structures on T1-weighted brain MRI: A Comparative Study of CNN, CNN hybrid-transformer or -Mamba architectures.

Background And Purpose: Recent advances in deep learning have shown promising results in medical image analysis and segmentation. However, most brain MRI segmentation models are limited by the size of their datasets and/or the number of structures they can identify. This study evaluates the performance of six advanced deep learning models in segmenting 122 brain structures from T1-weighted MRI scans, aiming to identify the most effective model for clinical and research applications.

Dynamic multispectral NIR/SWIR for lymphovascular architectural and functional quantification.

Significance: Although the lymphatic system is the second largest circulatory system in the body, there are limited techniques available for characterizing lymphatic vessel function. We report shortwave-infrared (SWIR) imaging for minimally invasive quantification of lymphatic circulation with superior contrast and resolution compared with near-infrared first window imaging.

Aim: We aim to study the lymphatic structure and function via SWIR fluorescence imaging.

Performance analysis of hybrid deep learning framework using a vision transformer and convolutional neural network for handwritten digit recognition.

Digitization created a demand for highly efficient handwritten document recognition systems. A handwritten document consists of digits, text, symbols, diagrams, etc. Digits are an essential element of handwritten documents.