ArticlePhysica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)2018
Segmentation of parotid glands from registered CT and MR images.
Article in Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for Head and Neck Cancer: A Safe Bet?-A Clinically Oriented Systematic Review for the Radiation Oncologist.Frontiers in oncology · 2021Pooled it
- Auto-segmentation of organs-of-interest clinical acceptability & reproducibility framework in head and neck cancer.Physics and imaging in radiation oncology · 2026Article
- Developing a segmentation cascade deep learning network based on automated prompts.Quantitative imaging in medicine and surgery · 2026Article
- Parotid Gland Segmentation Using Purely Transformer-Based U-Shaped Network and Multimodal MRI.Annals of biomedical engineering · 2024Article
- Review and recommendations on deformable image registration uncertainties for radiotherapy applications.Physics in medicine and biology · 2023Review
- Unsupervised deep learning registration model for multimodal brain images.Journal of applied clinical medical physics · 2023Article
- Deep-Learning-Based Automatic Segmentation of Parotid Gland on Computed Tomography Images.Diagnostics (Basel, Switzerland) · 2023Article
- Validation of clinical acceptability of deep-learning-based automated segmentation of organs-at-risk for head-and-neck radiotherapy treatment planning.Frontiers in oncology · 2023Article
- Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study.Journal of medical Internet research · 2021Article
- Cross-modality deep learning: Contouring of MRI data from annotated CT data only.Medical physics · 2021Article
- Feasibility of Continual Deep Learning-Based Segmentation for Personalized Adaptive Radiation Therapy in Head and Neck Area.Cancers · 2021Article
- Computerized Tomography Image Feature under Convolutional Neural Network Algorithm Evaluated for Therapeutic Effect of Clarithromycin Combined with Salmeterol/Fluticasone on Chronic Obstructive Pulmonary Disease.Journal of healthcare engineering · 2021Article
- Automatic multi-organ segmentation in computed tomography images using hierarchical convolutional neural network.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Preoperative evaluation and treatment consideration of parotid gland tumors.Laryngoscope investigative otolaryngology · 2020Article
- A Combined Loss-driven Framework for Automated Parotid Segmentation in Head-and-Neck Computed Tomography.Journal of medical physicsArticle
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7 authors.
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Abstract
purposeTo develop an automatic multimodal method for segmentation of parotid glands (PGs) from pre-registered computed tomography (CT) and magnetic resonance (MR) images and compare its results to the results of an existing state-of-the-art algorithm that segments PGs from CT images only.
methodsMagnetic resonance images of head and neck were registered to the accompanying CT images using two different state-of-the-art registration procedures. The reference domains of registered image pairs were divided on the complementary PG regions and backgrounds according to the manual delineation of PGs on CT images, provided by a physician. Patches of intensity values from both image modalities, centered around randomly sampled voxels from the reference domain, served as positive or negative samples in the training of the convolutional neural network (CNN) classifier. The trained CNN accepted a previously unseen (registered) image pair and classified its voxels according to the resemblance of its patches to the patches used for training. The final segmentation was refined using a graph-cut algorithm, followed by the dilate-erode operations.
resultsUsing the same image dataset, segmentation of PGs was performed using the proposed multimodal algorithm and an existing monomodal algorithm, which segments PGs from CT images only. The mean value of the achieved Dice overlapping coefficient for the proposed algorithm was 78.8%, while the corresponding mean value for the monomodal algorithm was 76.5%.
conclusionsAutomatic PG segmentation on the planning CT image can be augmented with the MR image modality, leading to an improved RT planning of head and neck cancer.
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