ReviewMedical physics2020
Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state-of-art applications.
Review in Medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
90 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence for segmentation and classification in lumbar spinal stenosis: an overview of current methods.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2025Pooled it
- The current state of autonomous suturing: a systematic review.Surgical endoscopy · 2024Pooled it
- Value of Rehabilitation Training for Children with Cerebral Palsy Diagnosed and Analyzed by Computed Tomography Imaging Information Features under Deep Learning.Journal of healthcare engineering · 2021Trial
- A two-stage deep learning system for cervical spine fracture diagnosis: integrating 3D segmentation and 2.5D classification on CT images.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Quality Assurance Strategies for Brain State Characterization by MEMRI.bioRxiv : the preprint server for biology · 2026Article
- Self-supervised pretraining with NuSPIRe unlocks nuclear morphology-driven insights in spatial omics.Genome biology · 2026Article
- Enhanced Pelvic CT Segmentation via Deep Learning: A Study on Loss Function Effects.Journal of imaging informatics in medicine · 2026Article
- PMCanalSeg: A dataset for automatic segmentation of the pterygopalatine and mandibular canals from 3D CBCT images.Scientific data · 2026Article
- Enabling earlier detection of spinal lesions in CT imaging with artificial intelligence-a case study.Frontiers in artificial intelligence · 2026Article
- Deep Learning Tools for Single Quantum Dot Tracking.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Segmentation of cortical bone, trabecular bone, and medullary pores from micro-CT images using 2D and 3D deep learning models.Anatomical record (Hoboken, N.J. : 2007) · 2025Article
- Differential effects of demographics and risk factors on the nonlinear orthotropic mechanical properties of human femoropopliteal arteries.Biomechanics and modeling in mechanobiology · 2025Article
- Adaptive fluorescence lifetime imaging with per-pixel signal optimization and flexible scanning.Biomedical optics express · 2025Article
- A hybrid convolutional neural network model for dental age estimation using buccal alveolar bone level for Saudi children.BMC oral health · 2025Article
- High-Resolution Ultrasound Data for AI-Based Segmentation in Mouse Brain Tumor.Scientific data · 2025Article
- AI-enhanced patient-specific dosimetry in I-131 planar imaging with a single oblique view.Scientific reports · 2025Article
- Supervised and unsupervised learning for lung perfusion data segmentation in electrical impedance tomography.Biomedical physics & engineering express · 2025Article
- Assessing the value of artificial intelligence-based image analysis for pre-operative surgical planning of neck dissections and iENE detection in head and neck cancer patients.Discover oncology · 2025Article
- Comparative analysis of ensemble learning techniques for enhanced fatigue life prediction.Scientific reports · 2025Article
- Bridging imaging-based in vitro methods from biomedical research to regulatory toxicology.Archives of toxicology · 2025Review
30 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
In recent years, significant progress has been made in developing more accurate and efficient machine learning algorithms for segmentation of medical and natural images. In this review article, we highlight the imperative role of machine learning algorithms in enabling efficient and accurate segmentation in the field of medical imaging. We specifically focus on several key studies pertaining to the application of machine learning methods to biomedical image segmentation. We review classical machine learning algorithms such as Markov random fields, k-means clustering, random forest, etc. Although such classical learning models are often less accurate compared to the deep-learning techniques, they are often more sample efficient and have a less complex structure. We also review different deep-learning architectures, such as the artificial neural networks (ANNs), the convolutional neural networks (CNNs), and the recurrent neural networks (RNNs), and present the segmentation results attained by those learning models that were published in the past 3 yr. We highlight the successes and limitations of each machine learning paradigm. In addition, we discuss several challenges related to the training of different machine learning models, and we present some heuristics to address those challenges.
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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 OpenQuestion graph.