1 Anhui University of Chinese Medicine, Hefei, China 2 College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China Medical image segmentation is fundamental ...

A deep learning-based medical imaging project for automatic brain tumor segmentation from multi-modal MRI scans using a 3D nnU-Net architecture with a Flask web interface.

Tumor segmentation in lung CT using U-Net, U-Net++ and an augmentation-enhanced U-Net. Best validation Dice: 0.807 (MSD lung dataset).

ABSTRACT: Brain tumor segmentation is a vital step in diagnosis, treatment planning, and prognosis in neuro-oncology. In recent years, deep learning approaches have revolutionized this field, evolving ...

Brain tumor segmentation is a vital step in diagnosis, treatment planning, and prognosis in neuro-oncology. In recent years, deep learning approaches have revolutionized this field, evolving from the ...

Purpose: Brain tumor segmentation with MRI is a challenging task, traditionally relying on manual delineation of regions-of-interest across multiple imaging sequences. However, this data-intensive ...

Trained on multi-hospital data, iSeg spots moving tumors doctors sometimes miss, edging radiation treatment toward pinpoint perfection. Credit: Stock An AI system called iSeg is reshaping radiation ...

Researchers from Science Tokyo develop a Multi-scale Hessian-enhanced Patch-based Neural Network Model for Segmentation of Liver Tumor from CT Scans. Liver cancer is the sixth most common cancer ...

Abstract: Tumor segmentation is crucial for surgical planning and precise tumor resection for effective treatment. Traditionally, tumor localization has been performed using medical imaging techniques ...