Label-set impact on deep learning-based prostate segmentation on MRI.

Background: Prostate segmentation is an essential step in computer-aided detection and diagnosis systems for prostate cancer. Deep learning (DL)-based methods provide good performance for prostate gland and zones segmentation, but little is known about the impact of manual segmentation (that is, label) selection on their performance. In this work, we investigated these effects by obtaining two different expert label-sets for the PROSTATEx I challenge training dataset (n = 198) and using them, in addition to an in-house dataset (n = 233), to assess the effect on segmentation performance.

The impact of pre-processing and disease characteristics on reproducibility of T2-weighted MRI radiomics features.

Purpose: To evaluate the reproducibility of radiomics features derived via different pre-processing settings from paired T2-weighted imaging (T2WI) prostate lesions acquired within a short interval, to select the setting that yields the highest number of reproducible features, and to evaluate the impact of disease characteristics (i.e., clinical variables) on features reproducibility.

CROPro: a tool for automated cropping of prostate magnetic resonance images.

Purpose: To bypass manual data preprocessing and optimize deep learning performance, we developed and evaluated CROPro, a tool to standardize automated cropping of prostate magnetic resonance (MR) images.

Approach: CROPro enables automatic cropping of MR images regardless of patient health status, image size, prostate volume, or pixel spacing. CROPro can crop foreground pixels from a region of interest (e.

Artificial intelligence for prostate MRI: open datasets, available applications, and grand challenges.

Artificial intelligence (AI) for prostate magnetic resonance imaging (MRI) is starting to play a clinical role for prostate cancer (PCa) patients. AI-assisted reading is feasible, allowing workflow reduction. A total of 3,369 multi-vendor prostate MRI cases are available in open datasets, acquired from 2003 to 2021 in Europe or USA at 3 T (n = 3,018; 89.

Pseudo-T2 mapping for normalization of T2-weighted prostate MRI.

Objective: Signal intensity normalization is necessary to reduce heterogeneity in T2-weighted (T2W) magnetic resonance imaging (MRI) for quantitative analysis of multicenter data. AutoRef is an automated dual-reference tissue normalization method that normalizes transversal prostate T2W MRI by creating a pseudo-T2 map. The aim of this study was to evaluate the accuracy of pseudo-T2s and multicenter standardization performance for AutoRef with three pairs of reference tissues: fat/muscle (AutoRef), femoral head/muscle (AutoRef) and pelvic bone/muscle (AutoRef).

The Reproducibility of Deep Learning-Based Segmentation of the Prostate Gland and Zones on T2-Weighted MR Images.

Volume of interest segmentation is an essential step in computer-aided detection and diagnosis (CAD) systems. Deep learning (DL)-based methods provide good performance for prostate segmentation, but little is known about the reproducibility of these methods. In this work, an in-house collected dataset from 244 patients was used to investigate the intra-patient reproducibility of 14 shape features for DL-based segmentation methods of the whole prostate gland (WP), peripheral zone (PZ), and the remaining prostate zones (non-PZ) on T2-weighted (T2W) magnetic resonance (MR) images compared to manual segmentations.

A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI.

Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of magnetic resonance imaging (MRI). Fully automated segmentation of the prostate is a crucial step of CAD for prostate cancer, but visual inspection is still required to detect poorly segmented cases. The aim of this work was therefore to establish a fully automated quality control (QC) system for prostate segmentation based on T2-weighted MRI.

Automated reference tissue normalization of T2-weighted MR images of the prostate using object recognition.

Objectives: To develop and evaluate an automated method for prostate T2-weighted (T2W) image normalization using dual-reference (fat and muscle) tissue.

Materials And Methods: Transverse T2W images from the publicly available PROMISE12 (N = 80) and PROSTATEx (N = 202) challenge datasets, and an in-house collected dataset (N = 60) were used. Aggregate channel features object detectors were trained to detect reference fat and muscle tissue regions, which were processed and utilized to normalize the 3D images by linear scaling.

Relative Enhanced Diffusivity in Prostate Cancer: Protocol Optimization and Diagnostic Potential.

Background: Relative enhanced diffusivity (RED) is a potential biomarker for indirectly measuring perfusion in tissue using diffusion-weighted magnetic resonance imaging (MRI) with 3 b values.

Purpose: To optimize the RED MRI protocol for the prostate, and to investigate its potential for prostate cancer (PCa) diagnosis.

Study Type: Prospective.

High Resolution Microultrasound (μUS) Investigation of the Gastrointestinal (GI) Tract.

High resolution, microultrasound (μUS) scanning of the gastrointestinal (GI) tract has potential as an important transmural imaging modality to aid in diagnosis. Operating at higher frequencies than conventional clinical ultrasound instruments, μUS is capable of providing scanned images of the GI tract with higher resolution. To investigate the use of μUS for this application, a phantom which is cost effective, within ethical guidelines and, most importantly, similar in histology to the human GI tract is necessary.