An Unsupervised Learning-Based Regional Deformable Model for Automated Multi-Organ Contour Propagation.
The aim of this study is to evaluate a regional deformable model based on a deep unsupervised learning model for automatic contour propagation in breast cone-beam computed tomography–guided adaptive radiation therapy. A deep unsupervised learning model was introduced to map breast's tumor bed, clini...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 3; pp. 923 - 932 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164473105&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164473105 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2023 vid: 36 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 164473105 161582383 164473105 164473105 10.1007/s10278-023-00779-z 164473105 ppf: 923 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An Unsupervised Learning-Based Regional Deformable Model for Automated Multi-Organ Contour Propagation. aug: au: Liang, Xiaokun Dai, Jingjing Zhou, Xuanru Liu, Lin Zhang, Chulong Jiang, Yuming Li, Na Niu, Tianye Xie, Yaoqin Dai, Zhenhui Wang, Xuetao affil: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, Guangdong, China sug: subj: Image Processing, Computer Assisted Education Tomography, X-Ray Computed Education Learning Methods Models, Educational Breast Neoplasms Radiotherapy Human Deep Learning Retrospective Design Record Review Algorithms Funding Source ab: The aim of this study is to evaluate a regional deformable model based on a deep unsupervised learning model for automatic contour propagation in breast cone-beam computed tomography–guided adaptive radiation therapy. A deep unsupervised learning model was introduced to map breast's tumor bed, clinical target volume, heart, left lung, right lung, and spinal cord from planning computed tomography to cone-beam CT. To improve the traditional image registration method's performance, we used a regional deformable framework based on the narrow-band mapping, which can mitigate the effect of the image artifacts on the cone-beam CT. We retrospectively selected 373 anonymized cone-beam CT volumes from 111 patients with breast cancer. The cone-beam CTs are divided into three sets. 311 / 20 / 42 cone-beam CT images were used for training, validating, and testing. The manual contour was used as reference for the testing set. We compared the results between the reference and the model prediction for evaluating the performance. The mean Dice between manual reference segmentations and the model predicted segmentations for breast tumor bed, clinical target volume, heart, left lung, right lung, and spinal cord were 0.78 ± 0.09, 0.90 ± 0.03, 0.88 ± 0.04, 0.94 ± 0.03, 0.95 ± 0.02, and 0.77 ± 0.07, respectively. The results demonstrated a good agreement between the reference and the proposed contours. The proposed deep learning–based regional deformable model technique can automatically propagate contours for breast cancer adaptive radiotherapy. Deep learning in contour propagation was promising, but further investigation was warranted. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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