ArticleBioMed research international2021
Automatic Liver Segmentation in CT Images with Enhanced GAN and Mask Region-Based CNN Architectures.
Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Deep-Learning-based Automatic Segmentation and Quantitative Measurement of Normal Spleen in Chinese AdultsCurrent medical imaging · 2026Article
- Liver CT image segmentation network based on multi-scale feature fusion.Scientific reports · 2025Article
- Machine learning-based identification of contrast-enhancement phase of computed tomography scans.PloS one · 2024Article
- Age-related morphometrics of normal adrenal glands based on deep learning-aided segmentation.Heliyon · 2023Article
- Article
- A Review Paper about Deep Learning for Medical Image Analysis.Computational and mathematical methods in medicine · 2023Review
- Harmonization Strategies in Multicenter MRI-Based Radiomics.Journal of imaging · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Liver image segmentation has been increasingly employed for key medical purposes, including liver functional assessment, disease diagnosis, and treatment. In this work, we introduce a liver image segmentation method based on generative adversarial networks (GANs) and mask region-based convolutional neural networks (Mask R-CNN). Firstly, since most resulting images have noisy features, we further explored the combination of Mask R-CNN and GANs in order to enhance the pixel-wise classification. Secondly,
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.