دورية أكاديمية
Automatic segmentation of nasopharyngeal carcinoma on CT images using efficient UNet-2.5D ensemble with semi-supervised pretext task pretraining.
| العنوان: | Automatic segmentation of nasopharyngeal carcinoma on CT images using efficient UNet-2.5D ensemble with semi-supervised pretext task pretraining. |
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| المؤلفون: | Domoguen JKL; Computer Vision and Machine Intelligence Group, Department of Computer Science, University of the Philippines-Diliman, Quezon City, Philippines., Manuel JA; Division of Radiation Oncology, Department of Radiology, University of the Philippines-Philippine General Hospital, Manila, Philippines., Cañal JPA; Division of Radiation Oncology, Department of Radiology, University of the Philippines-Philippine General Hospital, Manila, Philippines., Naval PC Jr; Computer Vision and Machine Intelligence Group, Department of Computer Science, University of the Philippines-Diliman, Quezon City, Philippines. |
| المصدر: | Frontiers in oncology [Front Oncol] 2022 Nov 11; Vol. 12, pp. 980312. Date of Electronic Publication: 2022 Nov 11 (Print Publication: 2022). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Frontiers Research Foundation] Country of Publication: Switzerland NLM ID: 101568867 Publication Model: eCollection Cited Medium: Print ISSN: 2234-943X (Print) Linking ISSN: 2234943X NLM ISO Abbreviation: Front Oncol Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Original Publication: [Lausanne : Frontiers Research Foundation] |
| مستخلص: | Nasopharyngeal carcinoma (NPC) is primarily treated with radiation therapy. Accurate delineation of target volumes and organs at risk is important. However, manual delineation is time-consuming, variable, and subjective depending on the experience of the radiation oncologist. This work explores the use of deep learning methods to automate the segmentation of NPC primary gross tumor volume (GTVp) in planning computer tomography (CT) images. A total of sixty-three (63) patients diagnosed with NPC were included in this study. Although a number of studies applied have shown the effectiveness of deep learning methods in medical imaging, their high performance has mainly been due to the wide availability of data. In contrast, the data for NPC is scarce and inaccessible. To tackle this problem, we propose two sequential approaches. First we propose a much simpler architecture which follows the UNet design but using 2D convolutional network for 3D segmentation. We find that this specific architecture is much more effective in the segmentation of GTV in NPC. We highlight its efficacy over other more popular and modern architecture by achieving significantly higher performance. Moreover to further improve performance, we trained the model using multi-scale dataset to create an ensemble of models. However, the performance of the model is ultimately dependent on the availability of labelled data. Hence building on top of this proposed architecture, we employ the use of semi-supervised learning by proposing the use of a combined pre-text tasks. Specifically we use the combination of 3D rotation and 3D relative-patch location pre-texts tasks to pretrain the feature extractor. We use an additional 50 CT images of healthy patients which have no annotation or labels. By semi-supervised pretraining the feature extractor can be frozen after pretraining which essentially makes it much more efficient in terms of the number of parameters since only the decoder is trained. Finally it is not only efficient in terms of parameters but also data, which is shown when the pretrained model with only portion of the labelled training data was able to achieve very close performance to the model trained with the full labelled data. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Domoguen, Manuel, Cañal and Naval.) |
| References: | Front Oncol. 2020 Feb 19;10:166. (PMID: 32154168) Front Oncol. 2021 Sep 17;11:704850. (PMID: 34604039) Technol Cancer Res Treat. 2019 Jan-Dec;18:1533033819884561. (PMID: 31736433) J Am Med Inform Assoc. 1997 May-Jun;4(3):199-212. (PMID: 9147339) Front Oncol. 2017 Dec 20;7:315. (PMID: 29376025) Front Oncol. 2022 Mar 18;12:827991. (PMID: 35387126) Med Image Comput Comput Assist Interv. 2019 Oct;11767:384-393. (PMID: 32766570) Med Image Anal. 2019 May;54:280-296. (PMID: 30959445) Nat Methods. 2021 Feb;18(2):203-211. (PMID: 33288961) Med Phys. 2017 Dec;44(12):6341-6352. (PMID: 28940372) |
| فهرسة مساهمة: | Keywords: automatic volume segmentation; deep learning; nasopharyngeal carcinoma; pretext tasks; radiotherapy; semi-supervised learning |
| تواريخ الأحداث: | Date Created: 20221128 Latest Revision: 20221129 |
| رمز التحديث: | 20231215 |
| مُعرف محوري في PubMed: | PMC9691832 |
| DOI: | 10.3389/fonc.2022.980312 |
| PMID: | 36439414 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 2234-943X |
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| DOI: | 10.3389/fonc.2022.980312 |