Pushing the Resolution Limit of Stimulated Emission Depletion Optical Nanoscopy

Optical nanoscopy, also known as super-resolution optical microscopy, has provided scientists with the means to surpass the diffraction limit of light microscopy and attain new insights into nanoscopic structures and processes that were previously inaccessible. In recent decades, numerous studies have endeavored to enhance super-resolution microscopy in terms of its spatial (lateral) resolution, axial resolution, and temporal resolution. In this review, we discuss recent efforts to push the resolution limit of stimulated emission depletion (STED) optical nanoscopy across multiple dimensions, including lateral resolution, axial resolution, temporal resolution, and labeling precision. We introduce promising techniques and methodologies building on the STED concept that have emerged in the field, such as MINSTED, isotropic STED, and event-triggered STED, and evaluate their respective strengths and limitations. Moreover, we discuss trade-off relationships that exist in far-field optical microscopy and how they come about in STED optical nanoscopy. By examining the latest developments addressing these aspects, we aim to provide an updated overview of the current state of STED nanoscopy and its potential for future research.

AI Model for Detection of Abdominal Hemorrhage Lesions in Abdominal CT Images

Information technology has been actively utilized in the field of imaging diagnosis using artificial intelligence (AI), which provides benefits to human health. Readings of abdominal hemorrhage lesions using AI can be utilized in situations where lesions cannot be read due to emergencies or the absence of specialists; however, there is a lack of related research due to the difficulty in collecting and acquiring images. In this study, we processed the abdominal computed tomography (CT) database provided by multiple hospitals for utilization in deep learning and detected abdominal hemorrhage lesions in real time using an AI model designed in a cascade structure using deep learning, a subfield of AI. The AI model was used a detection model to detect lesions distributed in various sizes with high accuracy, and a classification model that could screen out images without lesions was placed before the detection model to solve the problem of increasing false positives owing to the input of images without lesions in actual clinical cases. The developed method achieved 93.22% sensitivity and 99.60% specificity.