Region-adaptive magnetic resonance image enhancement for improving CNN-based segmentation of the prostate and prostatic zones

Nonlinear Harmonics: A Gateway to Enhanced Image Contrast and Material Discrimination

Recent advancements in atomic force microscopy (AFM) have enabled detailed exploration of materials at the molecular and atomic levels. These developments, however, pose a challenge: the data generated by microscopic and spectroscopic experiments are increasing rapidly in both size and complexity. Extracting meaningful physical insights from these datasets is challenging, particularly for multilayer heterogeneous nanoscale structures. In this paper, an unsupervised approach is presented to enhance AFM image contrast by analyzing the nonlinear response of a cantilever interacting with a material's surface using a wavelet-based AFM. This method simultaneously measures different frequencies and harmonics in a single scan, without the need for additional hardware and exciting multiple cantilevers' eigenmodes. This developed AFM image contrast enhancement (AFM-ICE) approach employs unsupervised learning, image processing, and image fusion techniques. The method is applied to interpret complex multilayer structures consist of defects, deposited nanoparticles and heterogeneities. Its substantial capability is demonstrated to improve image contrast and differentiate between various components. This methodology can pave the way for rapid and precise determination of material properties with enhanced resolution.

Detecting the articular disk in magnetic resonance images of the temporomandibular joint using YOLO series

The purpose of this study was to construct an artificial intelligence object detection model to detect the articular disk from temporomandibular joint (TMJ) magnetic resonance (MR) images using YOLO series. The study included two experiments using datasets from different MR imaging machines. A total of 536 MR images were retrospectively examined. The performance of YOLOv5 and YOLOv8 in detecting the TMJ articular disk in both normal and displaced conditions was evaluated. The impact of image-processing techniques, such as histogram equalization (HE) and contrast-limited adaptive HE (CLAHE) on model performance, was also examined. The results showed that the YOLO series could detect the articular disk regardless of displacement, with superior performance on images of normal disk position. The results suggest the applicability of object detection models in improving the diagnosis of TMJ disorders.

Toward Ensuring Data Quality in Multi-Site Cancer Imaging Repositories

Cancer remains a major global health challenge, affecting diverse populations across various demographics. Integrating Artificial Intelligence (AI) into clinical settings to enhance disease outcome prediction presents notable challenges. This study addresses the limitations of AI-driven cancer care due to low-quality datasets by proposing a comprehensive three-step methodology to ensure high data quality in large-scale cancer-imaging repositories. Our methodology encompasses (i) developing a Data Quality Conceptual Model with specific metrics for assessment, (ii) creating a detailed data-collection protocol and a rule set to ensure data homogeneity and proper integration of multi-source data, and (iii) implementing a Data Integration Quality Check Tool (DIQCT) to verify adherence to quality requirements and suggest corrective actions. These steps are designed to mitigate biases, enhance data integrity, and ensure that integrated data meets high-quality standards. We applied this methodology within the INCISIVE project, an EU-funded initiative aimed at a pan-European cancer-imaging repository. The use-case demonstrated the effectiveness of our approach in defining quality rules and assessing compliance, resulting in improved data integration and higher data quality. The proposed methodology can assist the deployment of big data centralized or distributed repositories with data from diverse data sources, thus facilitating the development of AI tools.

ProLesA-Net: A multi-channel 3D architecture for prostate MRI lesion segmentation with multi-scale channel and spatial attentions

Prostate cancer diagnosis and treatment relies on precise MRI lesion segmentation, a challenge notably for small (<15 mm) and intermediate (15-30 mm) lesions. Our study introduces ProLesA-Net, a multi-channel 3D deep-learning architecture with multi-scale squeeze and excitation and attention gate mechanisms. Tested against six models across two datasets, ProLesA-Net significantly outperformed in key metrics: Dice score increased by 2.2%, and Hausdorff distance and average surface distance improved by 0.5 mm, with recall and precision also undergoing enhancements. Specifically, for lesions under 15 mm, our model showed a notable increase in five key metrics. In summary, ProLesA-Net consistently ranked at the top, demonstrating enhanced performance and stability. This advancement addresses crucial challenges in prostate lesion segmentation, enhancing clinical decision making and expediting treatment processes.

Deep learning downscaled high-resolution daily near surface meteorological datasets over East Asia

U-Net, a deep-learning convolutional neural network, is used to downscale coarse meteorological data. Based on 19 models from the Coupled Model Intercomparison Project Phase 6 and the Multi-Source Weather (MSWX) dataset, bias correction and UNet downscaling approaches are used to develop high resolution dataset over the East Asian region, referred to as Climate Change for East Asia with Bias corrected UNet Dataset (CLIMEA-BCUD). CLIMEA-BCUD provides nine meteorological variables including 2-m air temperature, 2-m daily maximum air temperature, 2-m daily minimum air temperature, precipitation, 10-m wind speed, 2-m relative humidity, 2-m specific humidity, downward shortwave radiation and downward longwave radiation with 0.1° horizontal resolution at daily intervals over the historical period of 1950-2014 and three future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) of 2015-2100. Validation against MSWX indicates that CLIMEA-BCUD shows reasonable performance in terms of climatology, and it is capable of simulating seasonal cycles and future changes well. It is suggested that CLIMEA-BCUD can promote the application of deep learning in climate research in the areas of climate change, hydrology, etc.

Time-Resolved 3D cardiopulmonary MRI reconstruction using spatial transformer network

The accurate visualization and assessment of the complex cardiac and pulmonary structures in 3D is critical for the diagnosis and treatment of cardiovascular and respiratory disorders. Conventional 3D cardiac magnetic resonance imaging (MRI) techniques suffer from long acquisition times, motion artifacts, and limited spatiotemporal resolution. This study proposes a novel time-resolved 3D cardiopulmonary MRI reconstruction method based on spatial transformer networks (STNs) to reconstruct the 3D cardiopulmonary MRI acquired using 3D center-out radial ultra-short echo time (UTE) sequences. The proposed reconstruction method employed an STN-based deep learning framework, which used a combination of data-processing, grid generator, and sampler. The reconstructed 3D images were compared against the start-of-the-art time-resolved reconstruction method. The results showed that the proposed time-resolved 3D cardiopulmonary MRI reconstruction using STNs offers a robust and efficient approach to obtain high-quality images. This method effectively overcomes the limitations of conventional 3D cardiac MRI techniques and has the potential to improve the diagnosis and treatment planning of cardiopulmonary disorders.