2018
DOI: 10.1016/j.ejmp.2018.05.006
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Fully automatic and robust segmentation of the clinical target volume for radiotherapy of breast cancer using big data and deep learning

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Cited by 137 publications
(118 citation statements)
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References 27 publications
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“…A significant shorter time for each fraction is possible if further improvement in the deformable registration step of the original contours is achieved. However, other alternatives are also possible for the generation of new contours for both tumor and OARs at each fraction, such as the use of atlas based methods [34] or convolutional neural networks [35,36]. The time spent in recontouring and generating a new treatment plan could also be used to acquire additional MR sequences for offline evaluation of treatment response (for instance, diffusion weighted MR [37]).…”
Section: Discussionmentioning
confidence: 99%
“…A significant shorter time for each fraction is possible if further improvement in the deformable registration step of the original contours is achieved. However, other alternatives are also possible for the generation of new contours for both tumor and OARs at each fraction, such as the use of atlas based methods [34] or convolutional neural networks [35,36]. The time spent in recontouring and generating a new treatment plan could also be used to acquire additional MR sequences for offline evaluation of treatment response (for instance, diffusion weighted MR [37]).…”
Section: Discussionmentioning
confidence: 99%
“…To date, automated workflow has been developed and implemented in various businesses but the pace of applying automation into health care is not as fast as expected. In the recent two decades, the rapid progress in computerized radiotherapy treatment planning systems has led to great interest in the possibility of automating radiotherapy workflow, which includes automated target delineation (auto-segmentation), automated treatment planning, automated real-time adaptive radiotherapy and automated quality assurance [20][21][22][23][24][25]. This study presents a well-designed fully automated planning algorithm for standardized whole-breast radiotherapy treatment plan generation with a large cohort of 99 patients.…”
Section: Discussionmentioning
confidence: 99%
“…Comparison setup and metrics: We use 3-fold cross-validation, separated at the patient level, to evaluate performance of our approach and the competitor methods. We compare against setups using only the CT appearance information [14,15] and setups using the CT with binary GTV/LN masks [3]. Finally, we also compare against setups using the CT + GTV/LN SDMs, which does not consider the OARs.…”
Section: Experiments and Resultsmentioning
confidence: 99%
“…However, to the best of our knowledge, no prior work, CNN-based or not, has addressed esophageal cancer CTV segmentation. Works on CTV segmentation of other cancer types mostly operate based on the RTCT appearance alone [14,15]. As shown in Fig.…”
Section: Introductionmentioning
confidence: 99%
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