SynthesisBiomedical engineering online2024
Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation.
Synthesis in Biomedical engineering online, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Balancing accuracy and efficiency in lumbar spine image segmentation using multi-scale attention and residual learning.Scientific reports · 2026Article
- Clinical application of AI for lung lobe segmentation and functional quantification with SPECT/CT: open-source versus custom models.Scientific reports · 2026Article
- Automated deep learning for detection and measurement of adrenal masses in contrast-enhanced abdominal CT.European radiology · 2026Article
- AI-Driven Bone and Marrow Segmentation on FLT-PET/CT: Technical Multi-organ Validation in AML and HCT.Research square · 2026Article
- Segment anything small for ultrasound: Enhancing segmentation with non-generative augmentation.PLOS digital health · 2026Article
- AI driven interactive extraction of tomographic ultrasound imaging sequences for anal sphincter defects.Scientific reports · 2026Article
- GoLoCo-Net: global-local guided contextual attention network for medical images segmentation.Scientific reports · 2026Article
- A Flexible Hybrid Quantum-classical Training Framework of Organ-at-Risk and Tumor Segmentation Models for Radiation Therapy Planning.Scientific reports · 2026Article
- SpatialDINO: A Self-Supervised 3D Vision Transformer that enables Segmentation and Tracking in Crowded Cellular Environments.bioRxiv : the preprint server for biology · 2026Article
- Clinically validated dataset of 435 human colons segmented from CT colonography.Scientific data · 2026Article
- Integration of dual-energy computed tomography and radiomics to improve noninvasive assessment of liver fibrosis: A retrospective study.Journal of clinical imaging science · 2026Article
- Automated segmentation of lymph node metastases in colorectal cancer based on convolutional KAN.Open medicine (Warsaw, Poland) · 2026Article
- Transformer-based and CNN-based models for clinically effective 2D and 3D pelvic bone segmentation in CT imaging.BMC musculoskeletal disorders · 2025Article
- Deep learning-based segmentation of gallbladder cancer on abdominal computed tomography scans: a multicenter study.Abdominal radiology (New York) · 2025Article
- A Method for Sesame (Plants (Basel, Switzerland) · 2025Article
- Weakly Supervised Gland Segmentation Based on Hierarchical Attention Fusion and Pixel Affinity Learning.Bioengineering (Basel, Switzerland) · 2025Article
- AI-powered segmentation of bifid mandibular canals using CBCT.BMC oral health · 2025Article
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Universal medical image segmentation via in-context cross-attention.Frontiers in artificial intelligence · 2025Article
- Challenges and opportunities to integrate artificial intelligence in radiation oncology: a narrative review.Ewha medical journal · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Accurate segmentation of multiple organs in the head, neck, chest, and abdomen from medical images is an essential step in computer-aided diagnosis, surgical navigation, and radiation therapy. In the past few years, with a data-driven feature extraction approach and end-to-end training, automatic deep learning-based multi-organ segmentation methods have far outperformed traditional methods and become a new research topic. This review systematically summarizes the latest research in this field. We searched Google Scholar for papers published from January 1, 2016 to December 31, 2023, using keywords "multi-organ segmentation" and "deep learning", resulting in 327 papers. We followed the PRISMA guidelines for paper selection, and 195 studies were deemed to be within the scope of this review. We summarized the two main aspects involved in multi-organ segmentation: datasets and methods. Regarding datasets, we provided an overview of existing public datasets and conducted an in-depth analysis. Concerning methods, we categorized existing approaches into three major classes: fully supervised, weakly supervised and semi-supervised, based on whether they require complete label information. We summarized the achievements of these methods in terms of segmentation accuracy. In the discussion and conclusion section, we outlined and summarized the current trends in multi-organ segmentation.
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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.