This opinion piece discusses potential funding opportunities by the NIH BRAIN initiative to support the development of deep imaging technologies.
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| http://dx.doi.org/10.1117/1.JBO.19.3.030601 | DOI Listing |
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Pointer meters recognition method in the wild based on innovative deep learning techniques.
Sci Rep
January 2025
College of Computer and Data Science, Minjiang University, Fuzhou, 350018, China.
This study presents a novel approach to identifying meters and their pointers in modern industrial scenarios using deep learning. We developed a neural network model that can detect gauges and one or more of their pointers on low-quality images. We use an encoder network, jump connections, and a modified Convolutional Block Attention Module (CBAM) to detect gauge panels and pointer keypoints in images.
View Article and Find Full Text PDFMRI Free Targeting of Deep Brain Structures in the Anterior Cingulate Cortex Using Facial Landmarks.
Brain Stimul
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Department of Biomedical Engineering, 36 S Wasatch Dr, Salt Lake City, 84112, UT, United States.
Emerging neurostimulation methods aim to selectively modulate deep brain structures. Guiding these therapies has presented a substantial chal- lenge, since imaging modalities such as MRI limit the spectrum of benefi- ciaries. In this study, we assess the guidance accuracy of a neuronavigation method that does not require taking MRI scans.
View Article and Find Full Text PDFA foundation model with weak experiential guidance in detecting muscle invasive bladder cancer on MRI.
Cancer Lett
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Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, P.R. China, 210029; The Affiliated Suqian First People's Hospital of Nanjing Medical University, Suqian, Jiangsu Province, China. Electronic address:
Preoperative detection of muscle-invasive bladder cancer (MIBC) remains a great challenge in practice. We aimed to develop and validate a deep Vesical Imaging Network (ViNet) model for the detection of MIBC using high-resolution Tweighted MR imaging (hrTWI) in a multicenter cohort. ViNet was designed using a modified 3D ResNet, in which, the encoder layers were pretrained using a self-supervised foundation model on over 40,000 cross-modal imaging datasets for transfer learning, and the classification modules were weakly supervised by an experiential knowledge-domain mask indicated by a nnUNet segmentation model.
View Article and Find Full Text PDFInferring Multi-slice Spatially Resolved Gene Expression from H&E-stained Histology Images with STMCL.
Methods
January 2025
School of Information Science and Engineering, Yunnan University, Kunming, 650500, Yunnan, China. Electronic address:
Spatial transcriptomics has significantly advanced the measurement of spatial gene expression in the field of biology. However, the high cost of ST limits its application in large-scale studies. Using deep learning to predict spatial gene expression from H&E-stained histology images offers a more cost-effective alternative, but existing methods fail to fully leverage the multimodal information provided by Spatial transcriptomics and pathology images.
View Article and Find Full Text PDFConvolutional Neural Networks for the segmentation of hippocampal structures in postmortem MRI scans.
J Neurosci Methods
January 2025
Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA. Electronic address:
Background: The hippocampus plays a crucial role in memory and is one of the first structures affected by Alzheimer's disease. Postmortem MRI offers a way to quantify the alterations by measuring the atrophy of the inner structures of the hippocampus. Unfortunately, the manual segmentation of hippocampal subregions required to carry out these measures is very time-consuming.
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