Hyperspectral imaging (HSI) is demonstrating the growing capability for disease diagnosis and surgical cancer resection. That is mainly due to high spectral resolution of HSI when compared with its color (RGB) counterparts. However, increased spectral resolution is often associated with the loss of spatial resolution. That combined with high cost hinders applicability of HSI. Herein, we propose computational approach that attempts to mimic the HSI. It is using an approximate explicit feature map (aEFM) to augment raw and/or stain normalized RGB images of the hematoxylin and eosin stained histopathological specimen. We demonstrate on two public labeled datasets, related to breast cancer and nuclei, the statistically significant improvement of performance of binary (caner vs. non-cancer) segmentation of augmented RGB images in comparison with the results achieved on their RGB counterparts. For the breast cancer, balanced accuracy is increased from 76.56%±9.05% to 80.42%±9.23% and F1 score from 13.34%±6.46% to 17.33%±6.36%. For nuclei, balanced accuracy is increased from 68.68%±9.25% to 79.99%±8.77% and F1 score from 46.92%±15.10% to 63.31%±14.50%. While 0 constrained nonnegative matrix factorization was used for binary segmentation herein, we conjecture that aEFM based augmentation of RGB images can improve performance of more sophisticated segmentation methods such as deep networks.
Development of computer-aided diagnosis (CAD) systems is motivated by reduction of the workload on the pathologist that is increasing steadily. Among approaches upon which CAD-based systems are built, deep learning (DL) methods seem to be well suited for image analysis in digital pathology. However, DL networks include a large number of parameters and that requires a large annotated training dataset. Unfortunately, probably the biggest problem in digital pathology using machine learning methods is a small number of annotated images. That is especially true in intraoperative tissue analysis which coincides with the topic of the present paper: intraoperative CAD-based diagnosis of metastasis of colon cancer in a liver from hematoxylin-eosin (H and E) stained frozen section. To cope with the insufficiency of training images we adopt a transfer learning approach using the Nested UNet architecture. For better diagnostic performance, the trained model predicted pixels multiple times for different striding levels using the sliding window strategy. Threshold optimization using balanced accuracy score showed the validity of such an approach as balanced accuracy has increased significantly. When compared to often used UNet with VGG16 backbone, Nested UNet model with DenseNet201 backbone performs better on our dataset for both balanced accuracy metric and F1 score.
There is a need for computer-aided diagnosis (CAD) systems to relieve the workload on pathologists. This seems to be especially important for intraoperative diagnosis during surgery, for which diagnostic time is very limited. This paper presents preliminary results of intraoperative pixel-based CAD of colon cancer metastasis in a liver from phase-contrast images of unstained frozen sections. In particular, two deep learning networks: the U-net and the structured autoencoder for deep subspace clustering, were trained on eighteen phase-contrast images belonging to five patients and tested on eight images belonging to three patients. Spectrum angle mapper was also used in comparative performance analysis. The best result achieved by the U-net yielded balanced accuracy of 83.70%±8%, sensitivity of 94.50%±8%, specificity of 72.9%±8% and Dice coefficient of 45.20%±25.4%. However, factors such as absence of tissue fixation and ethanol-induced dehydration, melting of the specimen under the microscope and/or frozen crystals in the specimen cause variations in quality of phase-contrast images of unstained frozen sections. This, in return, affects reproducibility of diagnostic performance.
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