دورية أكاديمية
Digital workflows for pathological assessment of rat estrous cycle stage using images of uterine horn and vaginal tissue
| العنوان: | Digital workflows for pathological assessment of rat estrous cycle stage using images of uterine horn and vaginal tissue |
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| المؤلفون: | Shinichi Onishi, Riku Egami, Yuya Nakamura, Yoshinobu Nagashima, Kaori Nishihara, Saori Matsuo, Atsuko Murai, Shuji Hayashi, Yoshifumi Uesumi, Atsuhiko Kato, Hiroyuki Tsunoda, Masaki Yamazaki, Hideaki Mizuno |
| المصدر: | Journal of Pathology Informatics, Vol 13, Iss , Pp 100120- (2022) |
| بيانات النشر: | Elsevier, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics LCC:Pathology |
| مصطلحات موضوعية: | Digital workflow, Estrous cycle, Image recognition, Deep learning, Pathological assessment, Computer applications to medicine. Medical informatics, R858-859.7, Pathology, RB1-214 |
| الوصف: | Assessment of the estrous cycle of mature female mammals is an important component of verifying the efficacy and safety of drug candidates. The common pathological approach of relying on expert observation has several drawbacks, including laborious work and inter-viewer variability. The recent advent of image recognition technologies using deep learning is expected to bring substantial benefits to such pathological assessments. We herein propose 2 distinct deep learning-based workflows to classify the estrous cycle stage from tissue images of the uterine horn and vagina, respectively. These constructed models were able to classify the estrous cycle stages with accuracy comparable with that of expert pathologists. Our digital workflows allow efficient pathological assessments of the estrous cycle stage in rats and are thus expected to accelerate drug research and development. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2153-3539 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2153353922007143; https://doaj.org/toc/2153-3539 |
| DOI: | 10.1016/j.jpi.2022.100120 |
| URL الوصول: | https://doaj.org/article/877b08a55b0748739108377e4999497f |
| رقم الأكسشن: | edsdoj.877b08a55b0748739108377e4999497f |
| قاعدة البيانات: | Directory of Open Access Journals |
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Full Text Finder | تدمد: | 21533539 |
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| DOI: | 10.1016/j.jpi.2022.100120 |