Article

Automated Segmentation of Trunk Musculature with a Deep CNN Trained from Sparse Annotations in Radiation Therapy Patients with Metastatic Spine Disease

2025-01-20

Abstract excerpt

<h4>Purpose</h4> Given the high prevalence of vertebral fractures post-radiotherapy in patients with metastatic spine disease, accurate and rapid muscle segmentation could support efforts to quantify muscular changes due to disease or treatment and enable biomechanical modeling for assessments of vertebral loading to improve personalized evaluation of vertebral fracture risk. This study presents a deep-learning ap...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: bff6a700-1a9e-55b4-a1f8-ed0f5f8273d4
DOI: 10.1101/2025.01.13.25319967

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Automated Segmentation of Trunk Musculature with a Deep CNN Trained from Sparse Annotations in Radiation Therapy Patients with Metastatic Spine DiseaseDOI 10.1101/2025.01.13.25319967Read

Deep Learning Auto-Segmentation of Cervical Neck Skeletal Muscle for Sarcopenia Analysis Using Pre-Therapy CT in Patients with Head and Neck CancerSemantic proximity Read

Fully-automated sarcopenia assessment in head and neck cancer: development and external validation of a deep learning pipelineSemantic proximity Read

An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral musclesSemantic proximity Read

DeepACSA: Automatic segmentation of anatomical cross-sectional area in ultrasound images of human lower limb muscles using deep learningSemantic proximity Read

CT imaging-derived phenotypes for abdominal muscle and their association with age and sex in a medical biobank.Semantic proximity Read

Reliable Radiologic Skeletal Muscle Area Assessment - A Biomarker for Cancer Cachexia DiagnosisSemantic proximity Read

Generalizable deep learning framework for 3D medical image segmentation using limited training dataSemantic proximity Read

Automated 3D Segmentation of Human Vagus Nerve Fascicles and Epineurium from Micro-Computed Tomography Images Using Anatomy-Aware Neural NetworksSemantic proximity Read

A deep learning-based framework for standardized analysis of trabecular bone compartments from micro-CT imaging data in the mouse tibiaSemantic proximity Read

Automated L3 Skeletal Muscle Segmentation for Evaluation of Sarcopenia: Development and Independent Validation of an Ensemble-Based 2D nnU-Net Pipeline in a Complex Liver Disease CohortSemantic proximity Read

Muscle and Adipose Tissue Segmentations at the C3 Vertebral Level for Sarcopenia-Related Clinical Decision-Making in Patients with Head and Neck CancerSemantic proximity Read

Deep leaning auto-segmentation of tomographic datasets, repeatability and biomechanical outcomesSemantic proximity Read