65 citations · 72 across the 7 of their papers we have counts for
7 papers
Vendor-Agnostic Joint Relaxometry and Myelin Water Fraction Mapping with B1 and Motion Correction
Unay Dorken Gallastegi, Shohei Fujita, Yohan Jun +19
Obtaining consistent quantitative maps of myelin content and relaxation times across different sites and vendors is essential for advancing our understanding of brain development.…
AnyStar: Domain randomized universal star-convex 3D instance segmentation
Neel Dey, S. Mazdak Abulnaga, Benjamin Billot +4
Star-convex shapes arise across bio-microscopy and radiology in the form of nuclei, nodules, metastases, and other units. Existing instance segmentation networks for such structure…
Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets
Sheng He, Rina Bao, Jingpeng Li +4
Background: The segment-anything model (SAM), introduced in April 2023, shows promise as a benchmark model and a universal solution to segment various natural images. It comes with…
Segmentation Ability Map: Interpret deep features for medical image segmentation
Sheng He, Yanfang Feng, P. Ellen Grant +1
Deep convolutional neural networks (CNNs) have been widely used for medical image segmentation. In most studies, only the output layer is exploited to compute the final segmentatio…
Time-efficient, High Resolution 3T Whole Brain Quantitative Relaxometry using 3D-QALAS with Wave-CAIPI Readouts
Jaejin Cho, Borjan Gagoski, Tae Hyung Kim +9
Purpose: Volumetric, high-resolution, quantitative mapping of brain tissue relaxation properties is hindered by long acquisition times and signal-to-noise (SNR) challenges. This st…
Global-Local Transformer for Brain Age Estimation
Sheng He, P. Ellen Grant, Yangming Ou
Deep learning can provide rapid brain age estimation based on brain magnetic resonance imaging (MRI). However, most studies use one neural network to extract the global information…