WORD: Abdominal Organ Segmentation Benchmark from CT Images

WORD provides abdominal organ segmentation labels for CT images, developed by HiLab-git and published in Medical Image Analysis (MedIA) in 2022. It functions as a benchmark for clinical applicability and annotation-efficient learning in deep learning workflows.

Use Cases

• Training segmentation models using abdominal organ masks
• Benchmarking annotation-efficient learning strategies for medical imaging
• Clinical validation of organ segmentation algorithms on CT scans

Strengths

• Peer-reviewed research foundation (MedIA 2022)
• Focuses on annotation-efficient learning tasks
• Provides labels for abdominal organ segmentation

Limitations

• Total number of CT volumes and specific organ classes are not listed in the metadata
• Restricted to abdominal CT scans

Provenance

Source

Medical Image Analysis (MedIA) 2022 publication

Collection Method

annotated clinical CT images

Freshness

Last updated September 2024; based on 2022 research.