2026-03-09T08:32:27Z https://tokushima-u.repo.nii.ac.jp/oai

oai:tokushima-u.repo.nii.ac.jp:02009620 2025-04-28T05:09:18Z 1713853213384:1713853295607

Deep Regional Metastases Segmentation for Patient-Level Lymph Node Status Classification DRMS for Patient-Level Lymph Node Status Classification Wang, Lu 10380130 宋, 天 ソウ, テン Song, Tian 宋 ソウ Song 天 テン Tian 70848522 片山, 貴文 カタヤマ, タカフミ Katayama, Takafumi 片山 カタヤマ Katayama 貴文 タカフミ Takafumi Jiang, Xiantao 島本, 隆 シマモト, タカシ Shimamoto, Takashi 島本 シマモト Shimamoto 隆 タカシ Takashi Leu, Jenq-Shiou open access This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ Breast cancer metastases histological lymph node sections patient level analysis Generally, automatic diagnosis of the presence of metastases in lymph nodes has therapeutic implications for breast cancer patients. Detection and classification of breast cancer metastases have high clinical relevance, especially in whole-slide images of histological lymph node sections. Fast early detection leads to huge improvement of patient’s survival rate. However, currently pathologists mainly detect the metastases with microscopic assessments. This diagnosis procedure is extremely laborious and prone to inevitable missed diagnoses. Therefore, automated, accurate patient-level classification would hold great promise to reduce the pathologist’s workload while also reduce the subjectivity of diagnosis. In this paper, we provide a novel deep regional metastases segmentation (DRMS) framework for the patient-level lymph node status classification. First, a deep segmentation network (DSNet) is proposed to detect the regional metastases in patch-level. Then, we adopt the density-based spatial clustering of applications with noise (DBSCAN) to predict the whole metastases from individual slides. Finally, we determine patient-level pN-stages by aggregating each individual slide-level prediction. In combination with the above techniques, the framework can make better use of the multi-grained information in histological lymph node section of whole-slice images. Experiments on large-scale clinical datasets (e.g., CAMELYON17) demonstrate that our method delivers advanced performance and provides consistent and accurate metastasis detection in clinical trials. IEEE 2021-09-15 eng journal article VoR 382172 https://tokushima-u.repo.nii.ac.jp/records/2009620 https://doi.org/10.1109/ACCESS.2021.3113036 10.1109/ACCESS.2021.3113036 21693536 IEEE Access 9 129293 129302 https://tokushima-u.repo.nii.ac.jp/record/2009620/files/access_9_129293.pdf application/pdf 1.47 MB 2022-03-15