OBJECTIVE: A deep neural network (DNN) was trained to generate a multiparametric ultrasound (mpUS) volume from four input ultrasound-based modalities (acoustic radiation force impulse [ARFI] imaging, shear wave elasticity imaging [SWEI], quantitative...
Deep learning has become the de facto method for medical image segmentation, with 3D segmentation models excelling in capturing complex 3D structures and 2D models offering high computational efficiency. However, segmenting 2.5D images, characterized...
Journal of magnetic resonance imaging : JMRI
39540567
BACKGROUND: Artificial intelligence (AI) assistance may enhance radiologists' performance in detecting clinically significant prostate cancer (csPCa) on MRI. Further validation is needed for radiologists with different experiences.
OBJECTIVE: To create a machine-learning predictive model combining prostate imaging-reporting and data system (PI-RADS) score, PSA density, and clinical variables to predict clinically significant prostate cancer (csPCa).
BACKGROUND: Cognitive fusion MRI-guided targeted biopsy (cTB) has been widely used in the diagnosis of prostate cancer (PCa). However, cTB relies heavily on the operator's experience and confidence in MRI readings. Our objective was to compare the ca...
PURPOSE: To study the diagnostic image quality of high b-value diffusion weighted images (DWI) derived from standard and variably reduced datasets reconstructed with a commercially available deep learning reconstruction (DLR) algorithm.
Purpose To evaluate the performance of an artificial intelligence (AI) model in detecting overall and clinically significant prostate cancer (csPCa)-positive lesions on paired external and in-house biparametric MRI (bpMRI) scans and assess performanc...
Efficient extraction and analysis of histopathological images are crucial for accurate medical diagnoses, particularly for prostate cancer. This research enhances histopathological image reclamation by integrating Visual-Based Image Reclamation (VBIR...
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
39419353
BACKGROUND: Magnetic resonance imaging (MRI) is considered the gold standard for prostate segmentation. Computed tomography (CT)-based segmentation is prone to observer bias, potentially overestimating the prostate volume by ∼ 30 % compared to MRI. H...
Neural networks : the official journal of the International Neural Network Society
39388995
Magnetic resonance imaging (MRI) plays a pivotal role in diagnosing and staging prostate cancer. Precise delineation of the peripheral zone (PZ) and transition zone (TZ) within prostate MRI is essential for accurate diagnosis and subsequent artificia...