Intelligent recognition and segmentation of anatomical structures in spinal endoscopy: a deep learning approach with 1000 annotated images.

Deep Learning-Based Recognition of Hand Anatomical Structures and Traumatic Lesions in Surgical Images: A Proof-of-Concept Study.

BACKGROUND Computer vision in surgery remains limited by the complexity of anatomical environments and the scarcity of annotated datasets, particularly in hand surgery. Semantic segmentation could enable precise recognition of fine anatomical structures and lesions directly from surgical images. METHODS A dataset of...

A Deep Learning System for Automatic Localization of Anatomical Landmarks in X-rays to Assist in Diagnosis and Surgical Planning

Accurate localization of anatomical landmarks is crucial for clinical diagnosis and treatment assessment. However, existing Convolutional Neural Network (CNN)-based methods may result in global spatial information loss and consequent localization failures in the presence of complex anatomical structures or parenchy...

Deep Learning-Based Segmentation, Action, and Phase Recognition in Laparoscopic Cholecystectomy: A Review

The integration of artificial intelligence into biomedical image analysis represents a paradigm shift in clinical decision support and surgical informatics. Laparoscopic cholecystectomy (LC) is a widely performed minimally invasive procedure but remains technically challenging due to limited visibility, anatomical vari...

Current Medical Imaging

Automatic Kidney Image Segmentation During Robot-Assisted Partial Nephrectomy Using a Deep Learning Model Based on a Multiannotator Dataset: Model Development and Validation Study

Background Augmented reality (AR) has emerged as a promising tool to enhance surgical precision during robot-assisted partial nephrectomy (RAPN), particularly by enabling the intraoperative overlay of 3D anatomical models. However, real-time AR implementation requires robust image segmentation of anatomical structures,...

A Cascaded Deep Learning Framework for Robust Liver CT Segmentation Using ROI Refinement and Patient-Level Cross-Validation

A failure-aware cascaded deep learning framework for automated liver CT segmentation using the publicly available HCC-TACE-Seg dataset is presented and indicates that cascaded localisation and region-of-interest refinement can provide robust liver segmentation while reducing background interference and supporting uncer...