ArticleMedical image analysis2021
Capsules for biomedical image segmentation.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Multi-Mechanism Artificial Lemming Algorithm for Global Optimization and Color Multi-Threshold Image Segmentation.Biomimetics (Basel, Switzerland) · 2026Article
- 3-dimensional reconstruction and mixed reality in thoracic surgery: a narrative review and user guide.Journal of thoracic disease · 2025Review
- Capsule Neural Networks with Bayesian Optimization for Pediatric Pneumonia Detection from Chest X-Ray Images.Journal of clinical medicine · 2025Article
- ACPOA: An Adaptive Cooperative Pelican Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation.Biomimetics (Basel, Switzerland) · 2025Article
- MRI-based prostate cancer classification using 3D efficient capsule network.Medical physics · 2024Article
- Attention-gated 3D CapsNet for robust hippocampal segmentation.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Artificial intelligence and machine learning applications for the imaging of bone and soft tissue tumors.Frontiers in radiology · 2024Review
- The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review.NPJ digital medicine · 2023Review
- 3D Capsule Networks for Brain Image Segmentation.AJNR. American journal of neuroradiology · 2023Article
- A Review of Machine Learning Algorithms for Retinal Cyst Segmentation on Optical Coherence Tomography.Sensors (Basel, Switzerland) · 2023Review
- Joint Cancer Segmentation and PI-RADS Classification on Multiparametric MRI Using MiniSegCaps Network.Diagnostics (Basel, Switzerland) · 2023Article
- Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation.Bioengineering (Basel, Switzerland) · 2023Article
- Selecting the best optimizers for deep learning-based medical image segmentation.Frontiers in radiology · 2023Article
- Musculoskeletal MR Image Segmentation with Artificial Intelligence.Advances in clinical radiology · 2022Article
- Multi-Contrast MRI Segmentation Trained on Synthetic Images.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2022Article
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
- Recent Advanced Deep Learning Architectures for Retinal Fluid Segmentation on Optical Coherence Tomography Images.Sensors (Basel, Switzerland) · 2022Review
- CT-based radiomic nomogram for predicting the severity of patients with COVID-19.European journal of medical research · 2022Article
- Automated segmentation of biventricular contours in tissue phase mapping using deep learning.NMR in biomedicine · 2021Article
- Visualizing the dynamics of tuberculosis pathology using molecular imaging.The Journal of clinical investigation · 2021Review
Corrections and comments
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Authors and funding
5 authors.
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Abstract
Our work expands the use of capsule networks to the task of object segmentation for the first time in the literature. This is made possible via the introduction of locally-constrained routing and transformation matrix sharing, which reduces the parameter/memory burden and allows for the segmentation of objects at large resolutions. To compensate for the loss of global information in constraining the routing, we propose the concept of "deconvolutional" capsules to create a deep encoder-decoder style network, called SegCaps. We extend the masked reconstruction regularization to the task of segmentation and perform thorough ablation experiments on each component of our method. The proposed convolutional-deconvolutional capsule network, SegCaps, shows state-of-the-art results while using a fraction of the parameters of popular segmentation networks. To validate our proposed method, we perform experiments segmenting pathological lungs from clinical and pre-clinical thoracic computed tomography (CT) scans and segmenting muscle and adipose (fat) tissue from magnetic resonance imaging (MRI) scans of human subjects' thighs. Notably, our experiments in lung segmentation represent the largest-scale study in pathological lung segmentation in the literature, where we conduct experiments across five extremely challenging datasets, containing both clinical and pre-clinical subjects, and nearly 2000 computed-tomography scans. Our newly developed segmentation platform outperforms other methods across all datasets while utilizing less than 5% of the parameters in the popular U-Net for biomedical image segmentation. Further, we demonstrate capsules' ability to generalize to unseen handling of rotations/reflections on natural images.
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