ArticleRadiological physics and technology2018
Novel real-time tumor-contouring method using deep learning to prevent mistracking in X-ray fluoroscopy.
Article in Radiological physics and technology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 31 citations in OpenAlex.
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- Real-time markerless tumour tracking with patient-specific deep learning using a personalised data generation strategy: proof of concept by phantom study.The British journal of radiology · 2020Article
- Enabling machine learning in X-ray-based procedures via realistic simulation of image formation.International journal of computer assisted radiology and surgery · 2019Article
- Comments on "Novel real-time tumor-contouring method using deep learning to prevent mistracking in X-ray fluoroscopy" by Terunuma et al.Radiological physics and technology · 2018Article
- Response to "Comments on 'Novel real-time tumor-contouring method using deep learning to prevent mistracking in X-ray fluoroscopy"'.Radiological physics and technology · 2018Article
- Uncertain Feature-refinement Attention Unet: Considering Suitable Convolutional Neural Network Model for Real-time Segmentation in Markerless Tumor Tracking.Journal of medical physicsArticle
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3 authors at 1 institution in 1 country.
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Abstract
Robustness to obstacles is the most important factor necessary to achieve accurate tumor tracking without fiducial markers. Some high-density structures, such as bone, are enhanced on X-ray fluoroscopic images, which cause tumor mistracking. Tumor tracking should be performed by controlling "importance recognition": the understanding that soft-tissue is an important tracking feature and bone structure is unimportant. We propose a new real-time tumor-contouring method that uses deep learning with importance recognition control. The novelty of the proposed method is the combination of the devised random overlay method and supervised deep learning to induce the recognition of structures in tumor contouring as important or unimportant. This method can be used for tumor contouring because it uses deep learning to perform image segmentation. Our results from a simulated fluoroscopy model showed accurate tracking of a low-visibility tumor with an error of approximately 1 mm, even if enhanced bone structure acted as an obstacle. A high similarity of approximately 0.95 on the Jaccard index was observed between the segmented and ground truth tumor regions. A short processing time of 25 ms was achieved. The results of this simulated fluoroscopy model support the feasibility of robust real-time tumor contouring with fluoroscopy. Further studies using clinical fluoroscopy are highly anticipated.
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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.