Dynamic laser speckle phenomenon allows a contactless and nondestructive way to monitor biological changes that are quantified by second-order statistics applied in the images in time using a secondary matrix known as time history of the speckle pattern (THSP). To avoid being time consuming, the traditional way to build the THSP restricts the data to a line or column. Our hypothesis is that the spatial restriction of the information could compromise the results, particularly when undesirable and unexpected optical inhomogeneities occur, such as in cell culture media. It tested a spatial random approach to collect the points to form a THSP. Cells in a culture medium and in drying paint, representing homogeneous samples in different levels, were tested, and a comparison with the traditional method was carried out. An alternative random selection based on a Gaussian distribution around a desired position was also presented. The results showed that the traditional protocol presented higher variation than the outcomes using the random method. The higher the inhomogeneity of the activity map, the higher the efficiency of the proposed method using random points. The Gaussian distribution proved to be useful when there was a well-defined area to monitor.

Kidney stone disease is a prevalent condition associated with an increased risk of chronic kidney failure. Non-contrast Computed Tomography (CT) remains the gold standard for diagnosis due to its high sensitivity and ability to evaluate clinically relevant stone parameters. However, manual segmentation of stones in CT images is time-consuming and subject to inter-observer variability, highlighting the need for automated solutions. This paper introduces the KSSD2025, a novel dataset comprising 838 manually annotated axial CT images for kidney stone segmentation. We conducted a comprehensive evaluation of four segmentation models based on modified U-Net architectures: U-Net, U-Net++, U-Net3+, and TransU-Net. We evaluated their performance through 5-fold cross-validation and external tests. All models achieved mean Dice scores above 95%, with modified U-Net reaching 97.06% during cross-validation and modified U-Net3+ showing better generalization to external data. Our results confirm that U-Net-based architectures, when trained on high-quality annotations, can achieve accurate and reliable kidney stone segmentation. Our findings suggest that the KSSD2025 dataset offers a valuable resource to advance research in kidney stone analysis and the deployment of AI-assisted diagnostic tools in clinical practice.