ArticleNature communications2020
Glass-cutting medical images via a mechanical image segmentation method based on crack propagation.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Integrated biomimetic bioprinting of perichondrium with cartilage for auricle reconstruction.Bioactive materials · 2025Article
- General lightweight framework for vision foundation model supporting multi-task and multi-center medical image analysis.Nature communications · 2025Article
- Tissue-specified reconstruction modeling of the head and neck structure and its application in simulating airway obstruction.Heliyon · 2025Article
- Laser Stripe Centerline Extraction Method for Deep-Hole Inner Surfaces Based on Line-Structured Light Vision Sensing.Sensors (Basel, Switzerland) · 2025Article
- Unenhanced computed tomography radiomics help detect endoleaks after endovascular repair of abdominal aortic aneurysm.European radiology · 2024Article
- Automatic Segmentation of Lumbar Spine MRI Images Based on Improved Attention U-Net.Computational intelligence and neuroscience · 2022Article
- Deep Learning-Based CT Imaging in the Diagnosis of Treatment Effect of Pulmonary Nodules and Radiofrequency Ablation.Computational intelligence and neuroscience · 2022Article
- Diagnosis of Heart Failure Complicated with Sleep Apnea Syndrome by Thoracic Computerized Tomography under Artificial Intelligence Algorithm.Computational and mathematical methods in medicine · 2022Article
- Application of Deep Convolution Network to Automated Image Segmentation of Chest CT for Patients With Tumor.Frontiers in oncology · 2021Article
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Authors and funding
4 authors.
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Abstract
Medical image segmentation is crucial in diagnosing and treating diseases, but automatic segmentation of complex images is very challenging. Here we present a method, called the crack propagation method (CPM), based on the principles of fracture mechanics. This unique method converts the image segmentation problem into a mechanical one, extracting the boundary information of the target area by tracing the crack propagation on a thin plate with grooves corresponding to the area edge. The greatest advantage of CPM is in segmenting images involving blurred or even discontinuous boundaries, a task difficult to achieve by existing auto-segmentation methods. The segmentation results for synthesized images and real medical images show that CPM has high accuracy in segmenting complex boundaries. With increasing demand for medical imaging in clinical practice and research, this method will show its unique potential.
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