Automatic detection of circulating tumor cells and cancer associated fibroblasts using deep learning.
Automatic detection of circulating tumor cells and cancer associated fibroblasts using deep learning.
复制标题
利用深度学习自动检测循环肿瘤细胞和癌症相关成纤维细胞。
DOI：
10.1038/s41598-023-32955-0
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发表时间：
2023-04-07
影响因子：
4.6
通讯作者：
Yang, Changhuei
中科院分区：
文献类型：
--
作者：
Shen, Cheng;Rawal, Siddarth;Brown, Rebecca;Zhou, Haowen;Agarwal, Ashutosh;Watson, Mark A. A.;Cote, Richard J. J.;Yang, Changhuei
来自全血的循环肿瘤细胞（CTC）和癌症相关成纤维细胞（CAF）正在成为重要的生物标志物，可能有助于癌症诊断和预后。微过滤器技术为它们提供了一个有效的捕获平台，但受到两个挑战的困扰。首先，不均匀的微过滤器表面使得商业扫描仪难以获得所有细胞聚焦的图像。第二，目前的分析是劳动密集型的，周转时间长，用户之间的变化。在这里，我们通过开发定制的成像系统和数据预处理算法来解决第一个挑战。利用微过滤器捕获的培养的癌症和CAF细胞，我们发现我们定制系统的图像对焦率为99.3%，而顶级商业扫描仪的图像对焦率为89.9%。然后，我们开发了一种基于深度学习的方法来自动识别用于模拟CTC（mCTC）和CAF的肿瘤细胞。我们的深度学习方法实现了94%的准确率和召回率（± 0.2%）和96%（± 0.2%）用于mCTC检测，93%（± 1.7%）和84%（± 3.1%），显著优于传统的计算机视觉方法，mCTC为92%（± 0.2%）和78%（± 0.3%），CAF为58%（± 3.9%）和56%（± 3.5%）。我们的定制成像系统与深度学习细胞识别方法相结合，代表了CTC和CAF分析的重要进展。
Circulating tumor cells (CTCs) and cancer-associated fibroblasts (CAFs) from whole blood are emerging as important biomarkers that potentially aid in cancer diagnosis and prognosis. The microfilter technology provides an efficient capture platform for them but is confounded by two challenges. First, uneven microfilter surfaces makes it hard for commercial scanners to obtain images with all cells in-focus. Second, current analysis is labor-intensive with long turnaround time and user-to-user variability. Here we addressed the first challenge through developing a customized imaging system and data pre-processing algorithms. Utilizing cultured cancer and CAF cells captured by microfilters, we showed that images from our custom system are 99.3% in-focus compared to 89.9% from a top-of-the-line commercial scanner. Then we developed a deep-learning-based method to automatically identify tumor cells serving to mimic CTC (mCTC) and CAFs. Our deep learning method achieved precision and recall of 94% (± 0.2%) and 96% (± 0.2%) for mCTC detection, and 93% (± 1.7%) and 84% (± 3.1%) for CAF detection, significantly better than a conventional computer vision method, whose numbers are 92% (± 0.2%) and 78% (± 0.3%) for mCTC and 58% (± 3.9%) and 56% (± 3.5%) for CAF. Our custom imaging system combined with deep learning cell identification method represents an important advance on CTC and CAF analysis.
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影响因子：
4.6
作者：
Bankhead P;Loughrey MB;Fernández JA;Dombrowski Y;McArt DG;Dunne PD;McQuaid S;Gray RT;Murray LJ;Coleman HG;James JA;Salto-Tellez M;Hamilton PW
通讯作者：
Hamilton PW
影响因子：
--
作者：
Maertens Y;Humberg V;Erlmeier F;Steffens S;Steinestel J;Bögemann M;Schrader AJ;Bernemann C
通讯作者：
Bernemann C
影响因子：
11.2
作者：
Heitzer, Ellen;Auer, Martina;Speicher, Michael R.
通讯作者：
Speicher, Michael R.
DOI：
10.1109/tpami.2018.2858826
发表时间：
2020-02-01
影响因子：
23.6
作者：
Lin, Tsung-Yi;Goyal, Priya;Dollar, Piotr
通讯作者：
Dollar, Piotr
用于通用目标检测的深度学习：一项调查
影响因子：
19.5
作者：
Liu, Li;Ouyang, Wanli;Pietikainen, Matti
通讯作者：
Pietikainen, Matti