ArticleBiomedical optics express2019
Deriving external forces via convolutional neural networks for biomedical image segmentation.
Article in Biomedical optics express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Artificial intelligence for evaluation of magnetic resonance imaging-detected extramural vascular invasion in rectal cancer.PLOS digital health · 2026Article
- Bibliometric analysis of the application of artificial intelligence in orthopedic imaging.Quantitative imaging in medicine and surgery · 2025Article
- Using 2D U-Net convolutional neural networks for automatic acetabular and proximal femur segmentation of hip MRI images and morphological quantification: a preliminary study in DDH.Biomedical engineering online · 2024Article
- Efficient data labeling strategies for automated muscle segmentation in lower leg MRIs of Charcot-Marie-Tooth disease patients.PloS one · 2024Article
- A deep learning approach for orphan gene identification in moso bamboo (Phyllostachys edulis) based on the CNN + Transformer model.BMC bioinformatics · 2022Article
- Automated 3D Fetal Brain Segmentation Using an Optimized Deep Learning Approach.AJNR. American journal of neuroradiology · 2022Article
- Mask-RInternational journal of computer assisted radiology and surgery · 2021Article
- Fetal Ultrasound Image Segmentation for Automatic Head Circumference Biometry Using Deeply Supervised Attention-Gated V-Net.Journal of digital imaging · 2021Article
- IA-net: informative attention convolutional neural network for choroidal neovascularization segmentation in OCT images.Biomedical optics express · 2020Article
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Authors and funding
7 authors.
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Abstract
Active contours, or snakes, are widely applied on biomedical image segmentation. They are curves defined within an image domain that can move to object boundaries under the influence of internal forces and external forces, in which the internal forces are generally computed from curves themselves and external forces from image data. Designing external forces properly is a key point with active contour algorithms since the external forces play a leading role in the evolution of active contours. One of most popular external forces for active contour models is gradient vector flow (GVF). However, GVF is sensitive to noise and false edges, which limits its application area. To handle this problem, in this paper, we propose using GVF as reference to train a convolutional neural network to derive an external force. The derived external force is then integrated into the active contour models for curve evolution. Three clinical applications, segmentation of optic disk in fundus images, fluid in retinal optical coherence tomography images and fetal head in ultrasound images, are employed to evaluate the proposed method. The results show that the proposed method is very promising since it achieves competitive performance for different tasks compared to the state-of-the-art algorithms.
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