ArticleTomography (Ann Arbor, Mich.)2021
Evaluation of a Deep Learning Algorithm for Automated Spleen Segmentation in Patients with Conditions Directly or Indirectly Affecting the Spleen.
Article in Tomography (Ann Arbor, Mich.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed, 15 citations in OpenAlex.
- Progress in fully automated abdominal CT interpretation-an update over the past decade.Abdominal radiology (New York) · 2026Review
- Composite CT scoring system for Diffuse Idiopathic Skeletal Hyperostosis (DISH): a validated and reproducible framework for diagnosis and severity assessment.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Deep-Learning-based Automatic Segmentation and Quantitative Measurement of Normal Spleen in Chinese AdultsCurrent medical imaging · 2026Article
- Deep Learning Model for Automated Segmentation of Orbital Structures in MRI Images.Clinical neuroradiology · 2025Article
- Automated liver and spleen segmentation for MR elastography maps using U-Nets.Scientific reports · 2025Article
- Diagnostic ability of [European radiology · 2025Article
- SVNC-Net: An optimized U-Net variant with 2D convolutions for lightweight 3D spleen segmentation.PloS one · 2025Article
- Evaluation of manual and automated approaches for segmentation and extraction of quantitative indices from [Biomedical physics & engineering express · 2024Article
- Deep learning-enabled detection of hypoxic-ischemic encephalopathy after cardiac arrest in CT scans: a comparative study of 2D and 3D approaches.Frontiers in neuroscience · 2024Article
- A Deep-Learning Approach to Spleen Volume Estimation in Patients with Gaucher Disease.Journal of clinical medicine · 2023Article
- Two-Stage Deep Learning Model for Automated Segmentation and Classification of Splenomegaly.Cancers · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study was to develop a deep learning-based algorithm for fully automated spleen segmentation using CT images and to evaluate the performance in conditions directly or indirectly affecting the spleen (e.g., splenomegaly, ascites). For this, a 3D U-Net was trained on an in-house dataset (n = 61) including diseases with and without splenic involvement (in-house U-Net), and an open-source dataset from the Medical Segmentation Decathlon (open dataset, n = 61) without splenic abnormalities (open U-Net). Both datasets were split into a training (n = 32.52%), a validation (n = 9.15%) and a testing dataset (n = 20.33%). The segmentation performances of the two models were measured using four established metrics, including the Dice Similarity Coefficient (DSC). On the open test dataset, the in-house and open U-Net achieved a mean DSC of 0.906 and 0.897 respectively (
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.