Deep learning for histopathological segmentation of smooth muscle in the urinary bladder.
Deep learning for histopathological segmentation of smooth muscle in the urinary bladder.
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DOI：
10.1186/s12911-023-02222-3
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发表时间：
2023-07-15
影响因子：
3.5
通讯作者：
Cui, Feng
中科院分区：
文献类型：
--
作者：
Subramanya, Sridevi K.;Li, Rui;Wang, Ying;Miyamoto, Hiroshi;Cui, Feng
平滑肌的组织学评估是一个关键步骤，特别是在分期恶性肿瘤在各种内部器官，包括膀胱。尽管如此，病理学家对肌肉组织的手动分割和分类通常具有挑战性。因此，一个完全自动化和可靠的平滑肌图像分割系统是在很高的需求。为了表征膀胱中的肌纤维，包括粘膜肌层（MM）和固有肌层（MP），我们使用两个众所周知的深度学习（DL）模型组评估了来自手术标本的277个组织学图像，其中一个包括VGG 16，ResNet 18，SqueezeNet和MobileNetV 2，被认为是基于补丁的方法，另一个包括U-Net，MA-Net，DeepLabv 3+和FPN，被认为是基于像素的方法。两组中的所有训练模型都在像素级上进行了性能评估。对于分割MP和非MP（包括MM）区域，MobileNetV 2在基于补丁的方法中和U-Net在基于像素的方法中优于其在组中的同行，平均Jaccard指数分别等于0.74和0.79，平均Dice系数分别等于0.82和0.88。我们还展示了模型在速度和预测准确性方面的优势和劣势。这项工作不仅为平滑肌组织学分割工具的未来发展创造了基准，而且为膀胱癌的准确病理分期提供了一个有效的基于DL的诊断系统。在线版本包含补充材料，可通过10.1186/s12911-023-02222-3获得。
Histological assessment of smooth muscle is a critical step particularly in staging malignant tumors in various internal organs including the urinary bladder. Nonetheless, manual segmentation and classification of muscular tissues by pathologists is often challenging. Therefore, a fully automated and reliable smooth muscle image segmentation system is in high demand. To characterize muscle fibers in the urinary bladder, including muscularis mucosa (MM) and muscularis propria (MP), we assessed 277 histological images from surgical specimens, using two well-known deep learning (DL) model groups, one including VGG16, ResNet18, SqueezeNet, and MobileNetV2, considered as a patch-based approach, and the other including U-Net, MA-Net, DeepLabv3 + , and FPN, considered as a pixel-based approach. All the trained models in both the groups were evaluated at pixel-level for their performance. For segmenting MP and non-MP (including MM) regions, MobileNetV2, in the patch-based approach and U-Net, in the pixel-based approach outperformed their peers in the groups with mean Jaccard Index equal to 0.74 and 0.79, and mean Dice co-efficient equal to 0.82 and 0.88, respectively. We also demonstrated the strengths and weaknesses of the models in terms of speed and prediction accuracy. This work not only creates a benchmark for future development of tools for the histological segmentation of smooth muscle but also provides an effective DL-based diagnostic system for accurate pathological staging of bladder cancer. The online version contains supplementary material available at 10.1186/s12911-023-02222-3.
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影响因子：
64.8
作者：
Harris CR;Millman KJ;van der Walt SJ;Gommers R;Virtanen P;Cournapeau D;Wieser E;Taylor J;Berg S;Smith NJ;Kern R;Picus M;Hoyer S;van Kerkwijk MH;Brett M;Haldane A;Del Río JF;Wiebe M;Peterson P;Gérard-Marchant P;Sheppard K;Reddy T;Weckesser W;Abbasi H;Gohlke C;Oliphant TE
通讯作者：
Oliphant TE
影响因子：
5.7
作者：
Jimenez, Gabriel;Racoceanu, Daniel
通讯作者：
Racoceanu, Daniel
DOI：
10.1109/tpami.2017.2656884
发表时间：
2018-05
影响因子：
23.6
作者：
Chang H;Han J;Zhong C;Snijders AM;Mao JH
通讯作者：
Mao JH
DOI：
10.31449/inf.v44i3.2828
发表时间：
2020-09-01
期刊：
INFORMATICA-AN INTERNATIONAL JOURNAL OF COMPUTING AND INFORMATICS
影响因子：
--
作者：
Bozinovski, Stevo
通讯作者：
Bozinovski, Stevo
影响因子：
2
作者：
Elkady, Noha;Abdou, Asmaa Gaber;Ghanem, Noha
通讯作者：
Ghanem, Noha