دورية أكاديمية
أعرض في EDS

A Comparative Study of Automated Deep Learning Segmentation Models for Prostate MRI.

التفاصيل البيبلوغرافية
العنوان: A Comparative Study of Automated Deep Learning Segmentation Models for Prostate MRI.
المؤلفون: Rodrigues NM; LASIGE, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon, Portugal.; Champalimaud Foundation, Centre for the Unknown, 1400-038 Lisbon, Portugal., Silva S; LASIGE, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon, Portugal., Vanneschi L; NOVA Information Management School (NOVA IMS), Campus de Campolide, Universidade Nova de Lisboa, 1070-312 Lisboa, Portugal., Papanikolaou N; Champalimaud Foundation, Centre for the Unknown, 1400-038 Lisbon, Portugal.
المصدر: Cancers [Cancers (Basel)] 2023 Feb 25; Vol. 15 (5). Date of Electronic Publication: 2023 Feb 25.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: MDPI Country of Publication: Switzerland NLM ID: 101526829 Publication Model: Electronic Cited Medium: Print ISSN: 2072-6694 (Print) Linking ISSN: 20726694 NLM ISO Abbreviation: Cancers (Basel) Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: Basel, Switzerland : MDPI
مستخلص: Prostate cancer is one of the most common forms of cancer globally, affecting roughly one in every eight men according to the American Cancer Society. Although the survival rate for prostate cancer is significantly high given the very high incidence rate, there is an urgent need to improve and develop new clinical aid systems to help detect and treat prostate cancer in a timely manner. In this retrospective study, our contributions are twofold: First, we perform a comparative unified study of different commonly used segmentation models for prostate gland and zone (peripheral and transition) segmentation. Second, we present and evaluate an additional research question regarding the effectiveness of using an object detector as a pre-processing step to aid in the segmentation process. We perform a thorough evaluation of the deep learning models on two public datasets, where one is used for cross-validation and the other as an external test set. Overall, the results reveal that the choice of model is relatively inconsequential, as the majority produce non-significantly different scores, apart from nnU-Net which consistently outperforms others, and that the models trained on data cropped by the object detector often generalize better, despite performing worse during cross-validation.
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معلومات مُعتمدة: UIDB/00408/2020; UIDP/00408/2020; DSAIPA/DS/0113/2019; 2021/05322/BD. Fundação para a Ciência e Tecnologia
فهرسة مساهمة: Keywords: deep learning; prostate cancer; prostate detection; prostate segmentation
تواريخ الأحداث: Date Created: 20230311 Latest Revision: 20230313
رمز التحديث: 20240628
مُعرف محوري في PubMed: PMC10001231
DOI: 10.3390/cancers15051467
PMID: 36900261
قاعدة البيانات: MEDLINE
الوصف
تدمد:2072-6694
DOI:10.3390/cancers15051467