Evidence map›Paper›PMID 41757204›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Quality versus quantity of training datasets for artificial intelligence-based whole liver segmentation.

Austin Castelo, Caleb O'Connor, Aashish C Gupta, Brian M Anderson, McKell Woodland, Mais Altaie, Eugene J Koay, Bruno C Odisio, Tien T Tang, Kristy K Brock

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Austin CasteloDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.ORCID 0009-0009-3015-3570
Caleb O'ConnorDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Aashish C GuptaDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Brian M AndersonDepartment of Radiation Medicine and Applied Sciences, University of California, San Diego, San Diego, California, CA 92122, USA.
McKell WoodlandDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Mais AltaieDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Eugene J KoayDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Bruno C OdisioDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Tien T TangDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Kristy K BrockDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI DIANE BODURKA · 1985 to 2026
$290.8M
Project 3: Enhanced Sensitivity of Tumors to Proton Beam Therapy: Mechanisms and Biomarkers.P01CA261669 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI TITT, UWE · 2021 to 2025
$14.0M
Clinical Validation Center for Early Detection of Pancreatic CancerU01CA200468 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI ANIRBAN MAITRA · 2016 to 2026
$11.0M
Transport Oncophysics CoreU54CA210181 · NCI · METHODIST HOSPITAL RESEARCH INSTITUTE · PI CHANG, JENNY C-N, SHEN, HAIFA · 2016 to 2020
$9.3M
Imaging and Molecular Correlates of Progression in Cystic Neoplasms of the PancreasU01CA196403 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI MAITRA, ANIRBAN · 2015 to 2020
$5.8M
Anatomical Modeling to Improve the Precision of Image Guided Liver AblationR01CA235564 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI BROCK, KRISTY, ODISIO, BRUNO C · 2019 to 2023
$1.7M
NCI NIH HHS P01 CA261669NCI NIH HHS P30 CA016672NCI NIH HHS R01 CA235564NCI NIH HHS U01 CA196403NCI NIH HHS U01 CA200468NCI NIH HHS U54 CA210181
6 · The paper itself

Abstract

Artificial intelligence (AI) based segmentation has many medical applications but limited curated datasets challenge model training; this study compares the impact of dataset annotation quality and quantity on whole liver AI segmentation performance. We obtained 3,089 abdominal computed tomography scans with whole-liver contours from MD Anderson Cancer Center (MDA) and a MICCAI challenge. A total of 249 scans were withheld for testing of which 30, MICCAI challenge data, were reserved for external validation. The remaining scans were divided into mixed-curation and highly-curated groups, randomly sampled into sub-datasets of various sizes, and used to train 3D nnU-Net segmentation models. Dice similarity coefficients (DSC), surface DSC with 2mm margins (SD 2mm), the 95th percentile of Hausdorff distance (HD95), and 2D axial slice DSC (Slice DSC) were used to evaluate model performance. The highly curated, 244-scan model (DSC=0.971, SD 2mm=0.958, HD95=2.98mm) performed insignificantly different on 3D evaluation metrics to the mixed-curation 2,840-scan model (DSC=0.971 [

Indexed as

3D nnU-NetAI-based liver segmentationDataset qualityDeep learning

Identifiers

PMID41757204
PMCPMC12934849

What OpenQuestion holds

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Registered trials

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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 OpenQuestion graph.