In histological pathology, frozen sections are often used for rapid diagnosis during surgeries, as they can be produced within minutes. However, they suffer from artifacts and often lack crucial diagnostic details, particularly within the cell nuclei region. Permanent sections, on the other hand, contain more diagnostic detail but require a time-intensive preparation process. Here, we present a generative deep learning approach to enhance frozen section images by leveraging guidance from permanent sections. Our method places a strong emphasis on the nuclei region, which contains critical information in both frozen and permanent sections. Importantly, our approach avoids generating artificial data in blank regions, ensuring that the network only enhances existing features without introducing potentially unreliable information. We achieve this through a segmented attention network, incorporating nuclei-segmented images during training and adding an additional loss function to refine the nuclei details in the generated permanent images. We validated our method across various tissues, including kidney, breast, and colon. This approach significantly improves histological efficiency and diagnostic accuracy, enhancing frozen section images within seconds, and seamlessly integrating into existing laboratory workflows.

Determining the type of kidney stones allows urologists to prescribe a treatment to avoid recurrence of renal lithiasis. An automated in-vivo image-based classification method would be an important step towards an immediate identification of the kidney stone type required as a first phase of the diagnosis. In the literature it was shown on ex-vivo data (i.e., in very controlled scene and image acquisition conditions) that an automated kidney stone classification is indeed feasible. This pilot study compares the kidney stone recognition performances of six shallow machine learning methods and three deep-learning architectures which were tested with in-vivo images of the four most frequent urinary calculi types acquired with an endoscope during standard ureteroscopies. This contribution details the database construction and the design of the tested kidney stones classifiers. Even if the best results were obtained by the Inception v3 architecture (weighted precision, recall and F1-score of 0.97, 0.98 and 0.97, respectively), it is also shown that choosing an appropriate colour space and texture features allows a shallow machine learning method to approach closely the performances of the most promising deep-learning methods (the XGBoost classifier led to weighted precision, recall and F1-score values of 0.96). This paper is the first one that explores the most discriminant features to be extracted from images acquired during ureteroscopies.

Objective: To assess automatic computer-aided in-situ recognition of morphological features of pure and mixed urinary stones using intraoperative digital endoscopic images acquired in a clinical setting. Materials and methods: In this single-centre study, an experienced urologist intraoperatively and prospectively examined the surface and section of all kidney stones encountered. Calcium oxalate monohydrate (COM/Ia), dihydrate (COD/IIb) and uric acid (UA/IIIb) morphological criteria were collected and classified to generate annotated datasets. A deep convolutional neural network (CNN) was trained to predict the composition of both pure and mixed stones. To explain the predictions of the deep neural network model, coarse localisation heat-maps were plotted to pinpoint key areas identified by the network. Results: This study included 347 and 236 observations of stone surface and stone section, respectively. A highest sensitivity of 98 % was obtained for the type "pure IIIb/UA" using surface images. The most frequently encountered morphology was that of the type "pure Ia/COM"; it was correctly predicted in 91 % and 94 % of cases using surface and section images, respectively. Of the mixed type "Ia/COM+IIb/COD", Ia/COM was predicted in 84 % of cases using surface images, IIb/COD in 70 % of cases, and both in 65 % of cases. Concerning mixed Ia/COM+IIIb/UA stones, Ia/COM was predicted in 91 % of cases using section images, IIIb/UA in 69 % of cases, and both in 74 % of cases. Conclusions: This preliminary study demonstrates that deep convolutional neural networks are promising to identify kidney stone composition from endoscopic images acquired intraoperatively. Both pure and mixed stone composition could be discriminated. Collected in a clinical setting, surface and section images analysed by deep CNN provide valuable information about stone morphology for computer-aided diagnosis.