Home ›
Entities
› academia
› Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study
Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study
Research article (The Lancet Digital Health, 2020) · cited 158× · AI/ML
Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study
Summary
Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study is a scholarly article[1].
Key Facts
Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study. Retrieved May 24, 2026, from https://4ort.xyz/entity/multiclass-semantic-segmentation-and-quantification-of-traumatic-brain-injury-lesions-on-head-ct-using-deep-learning-an-
MLA“Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/multiclass-semantic-segmentation-and-quantification-of-traumatic-brain-injury-lesions-on-head-ct-using-deep-learning-an-.
BibTeX@misc{4ortxyz_multiclass-semantic-segmentation-and-quantification-of-traumatic-brain-injury-lesions-on-head-ct-using-deep-learning-an-_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study}}, year = {2026}, url = {https://4ort.xyz/entity/multiclass-semantic-segmentation-and-quantification-of-traumatic-brain-injury-lesions-on-head-ct-using-deep-learning-an-}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study — https://4ort.xyz/entity/multiclass-semantic-segmentation-and-quantification-of-traumatic-brain-injury-lesions-on-head-ct-using-deep-learning-an- (retrieved 2026-05-24)
