Deep Learning for Hemorrhagic Lesion Detection and Segmentation on Brain CT Images

Lu Li, Wei Meng, Bo Liu, Kunakorn C. Atchaneeyasakul, Fugen Zhou, Zehao Pan, Shimran A. Kumar, Jason Y. Zhang et al.

IEEE Journal of Biomedical and Health Informatics 2020 article cited by 171

Source More in Medicine & Health

importance 7/10 ai assisted Medical Image Segmentation Techniques stroke imaging stage: research

Problem

Locating hemorrhagic strokes in brain CT images is challenging and time-consuming even for experienced radiologists due to high variability in location, contrast, and shape.

Why it matters

Immediate, accurate hemorrhagic stroke diagnosis is crucial for emergency care and preventing death or disability.

What the AI did

A U-net based deep learning framework automatically detects and segments hemorrhage in CT brain images, using flipped-image concatenation to impose brain symmetry constraints.

Results

not stated in source

Impact

Peer-reviewed method with architecture comparisons; clinical performance metrics not stated in source.

Record

Venue	IEEE Journal of Biomedical and Health Informatics
Published	2020
Work type	article
Authors	Lu Li, Wei Meng, Bo Liu, Kunakorn C. Atchaneeyasakul, Fugen Zhou, Zehao Pan, Shimran A. Kumar, Jason Y. Zhang et al.
Citations	171 (OpenAlex, at import time)
References	61
Model or method	U-net based deep learning framework
Organization	not stated in source
Primary source	https://doi.org/10.1109/jbhi.2020.3028243
Open access	closed
Keywords	hemorrhagic stroke, brain CT, U-net, lesion segmentation, deep learning

Importance rating

7 / 10

significant — thousands to millions affected

Abstract states stroke likely causes long-term disabilities and death and that immediate accurate diagnosis is crucial for emergency care; major clinical problem, though no scale quantified.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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