The two major challenges to deep-learning-based medical image segmentation are multi-modality and a lack of expert annotations. Existing semi-supervised segmentation models can mitigate the problem of insufficient annotations by utilizing a small amo...
BACKGROUND: Cardiometabolic disorders pose significant health risks globally. Metabolic syndrome, characterized by a cluster of potentially reversible metabolic abnormalities, is a known risk factor for these disorders. Early detection and interventi...
Within the medical field of human assisted reproductive technology, a method for interpretable, non-invasive, and objective oocyte evaluation is lacking. To address this clinical gap, a workflow utilizing machine learning techniques has been develope...
Deep neural networks (DNNs) have been widely applied in medical image classification and achieve remarkable classification performance. These achievements heavily depend on large-scale accurately annotated training data. However, label noise is inevi...
In this study, our objective was to assess the performance of two deep learning-based hippocampal segmentation methods, SynthSeg and TigerBx, which are readily available to the public. We contrasted their performance with that of two established tech...
Temporally consistent and accurate registration and parcellation of longitudinal cortical surfaces is of great importance in studying longitudinal morphological and functional changes of human brains. However, most existing methods are developed for ...
BACKGROUND: Magnetic resonance imaging (MRI) is the gold standard for delineating cancerous lesions in soft tissue. Catheter-based interventions require the accurate placement of multiple long, flexible catheters at the target site. The manual segmen...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
May 7, 2024
Analyzing the basal ganglia following an early brain lesion is crucial due to their noteworthy role in sensory-motor functions. However, the segmentation of these subcortical structures on MRI is challenging in children and is further complicated by ...
Journal of applied clinical medical physics
May 7, 2024
PURPOSE: The aim of this study is to develop a deep learning model capable of discriminating between pancreatic plasma cystic neoplasms (SCN) and mucinous cystic neoplasms (MCN) by leveraging patient-specific clinical features and imaging outcomes. T...
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