ArticleScientific data2026
Clinically validated dataset of 435 human colons segmented from CT colonography.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-quality segmentation datasets are essential for advancing AI applications in medical imaging. However, it is challenging to generate such datasets for highly variable and complex organs like the colon. We introduce a dataset of 435 human colons, segmented from Computed Tomography Colonography (CTC) obtained from the publicly available The Cancer Imaging Archive (TCIA). Each scan includes a mask of the whole colon, including collapsed segments and the fluid, and a mask of only the gas-filled parts of the colon. The colon segmentation accuracy has been clinically validated by an expert abdominal radiologist. This is the first open-access dataset of segmented colons derived from CTC. This resource enables population-scale radiologic studies, supports the development of AI-based image analysis tools, and facilitates the creation of anatomically accurate digital models and simulators, both virtual and physical.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.