Bouchama Lyes, Dorizzi Bernadette, Thellier Marc, Klossa Jacques, Gottesman Yaneck
Samovar, Télécom SudParis, Institut Polytechnique de Paris, 91120 Palaiseau, France.
TRIBVN/T-life, 92800 Puteaux, France.
Biomed Opt Express. 2023 Jun 7;14(7):3172-3189. doi: 10.1364/BOE.489776. eCollection 2023 Jul 1.
Digital pathology based on a whole slide imaging system is about to permit a major breakthrough in automated diagnosis for rapid and highly sensitive disease detection. High-resolution FPM (Fourier ptychographic microscopy) slide scanners delivering rich information on biological samples are becoming available. They allow new effective data exploitation for efficient automated diagnosis. However, when the sample thickness becomes comparable to or greater than the microscope depth of field, we report an observation of undesirable contrast change of sub-cellular compartments in phase images around the optimal focal plane, reducing their usability. In this article, a bi-modal U-Net artificial neural network (i.e., a two channels U-Net fed with intensity and phase images) is trained to reinforce specifically targeted sub-cellular compartments contrast for both intensity and phase images. The procedure used to construct a reference database is detailed. It is obtained by exploiting the FPM reconstruction algorithm to explore images around the optimal focal plane with virtual Z-stacking calculations and selecting those with adequate contrast and focus. By construction and once trained, the U-Net is able to simultaneously reinforce targeted cell compartment visibility and compensate for any focus imprecision. It is efficient over a large field of view at high resolution. The interest of the approach is illustrated considering the use-case of detection in blood smear where improvement in the detection sensitivity is demonstrated without degradation of the specificity. Post-reconstruction FPM image processing with such U-Net and its training procedure is general and applicable to demanding biological screening applications.
基于全玻片成像系统的数字病理学即将在疾病快速检测和高灵敏度自动诊断方面取得重大突破。能够提供生物样本丰富信息的高分辨率傅里叶叠层显微镜（FPM）玻片扫描仪已逐渐问世。它们为高效自动诊断提供了新的有效数据利用方式。然而，当样本厚度与显微镜景深相当或更大时，我们发现，在最佳焦平面附近的相位图像中，亚细胞区室的对比度出现了不良变化，降低了其可用性。在本文中，我们训练了一种双模态U-Net人工神经网络（即一种输入强度图像和相位图像的双通道U-Net），专门增强强度图像和相位图像中特定目标亚细胞区室的对比度。详细介绍了构建参考数据库的过程。通过利用FPM重建算法，通过虚拟Z轴堆叠计算探索最佳焦平面周围的图像，并选择对比度和焦点合适的图像来获得该数据库。通过构建并经过训练后，U-Net能够同时增强目标细胞区室的可见性，并补偿任何聚焦不精确性。它在高分辨率的大视野范围内都很有效。考虑到血液涂片检测的用例，该方法的优势得到了体现，在该用例中，检测灵敏度得到了提高，而特异性并未降低。使用这种U-Net及其训练过程进行重建后的FPM图像处理具有通用性，适用于要求苛刻的生物筛选应用。
