A Multiscale Connected UNet for the Segmentation of Lung Cancer Cells in Pathology Sections Stained Using Rapid On-Site Cytopathological Evaluation

IF 4.7 2区 医学 Q1 PATHOLOGY American Journal of Pathology Pub Date : 2024-06-17 DOI:10.1016/j.ajpath.2024.05.011
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Abstract

Lung cancer is an increasingly serious health problem worldwide, and early detection and diagnosis are crucial for successful treatment. With the development of artificial intelligence and the growth of data volume, machine learning techniques can play a significant role in improving the accuracy of early detection in lung cancer. This study proposes a deep learning-based segmentation algorithm for rapid on-site cytopathological evaluation (ROSE) to enhance the diagnostic efficiency of endobronchial ultrasound-guided transbronchial needle aspiration biopsy (EBUS-TBNA) during surgery. By utilizing the CUNet3+ network model, cell clusters, including cancer cell clusters, can be accurately segmented in ROSE-stained pathological sections. The model demonstrated high accuracy, with an F1-score of 0.9604, recall of 0.9609, precision of 0.9654, and accuracy of 0.9834 on the internal testing data set. It also achieved an area under the receiver-operating characteristic curve of 0.9972 for cancer identification. The proposed algorithm saved time for on-site diagnosis, improved EBUS-TBNA efficiency, and outperformed classical segmentation algorithms in accurately identifying lung cancer cell clusters in ROSE-stained images. It effectively reduced over-segmentation, decreased network parameters, and enhanced computational efficiency, making it suitable for real-time patient evaluation during surgical procedures.

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CUNet3+:用于现场快速细胞病理学评估的病理切片染色中肺癌细胞分段的多尺度连接 UNet。
肺癌是全球日益严重的健康问题,早期发现和诊断是成功治疗的关键。随着人工智能的发展和数据量的增长,机器学习技术在提高肺癌早期检测的准确性方面可以发挥重要作用。本研究提出了一种基于深度学习的现场细胞病理学快速评估(ROSE)分割算法,以提高手术中支气管内超声引导下经支气管针吸活检(EBUS-TBNA)的诊断效率。通过利用 CUNet3+ 网络模型,可以在 ROSE 染色的病理切片中准确分割细胞群,包括癌细胞群。该模型具有很高的准确性,在内部测试数据集上的 F1 分数为 0.9604,召回率为 0.9609,精确度为 0.9654,准确度为 0.9834。癌症识别的 AUC 也达到了 0.9972。该算法节省了现场诊断的时间,提高了 EBUS-TBNA 的效率,在准确识别 ROSE 染色图像中的肺癌细胞簇方面优于传统的分割算法。它有效地减少了过度分割,降低了网络参数,提高了计算效率,适用于手术过程中对病人的实时评估。
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来源期刊
CiteScore
11.40
自引率
0.00%
发文量
178
审稿时长
30 days
期刊介绍: The American Journal of Pathology, official journal of the American Society for Investigative Pathology, published by Elsevier, Inc., seeks high-quality original research reports, reviews, and commentaries related to the molecular and cellular basis of disease. The editors will consider basic, translational, and clinical investigations that directly address mechanisms of pathogenesis or provide a foundation for future mechanistic inquiries. Examples of such foundational investigations include data mining, identification of biomarkers, molecular pathology, and discovery research. Foundational studies that incorporate deep learning and artificial intelligence are also welcome. High priority is given to studies of human disease and relevant experimental models using molecular, cellular, and organismal approaches.
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