Validation of a surface-based deformable MRI-3D ultrasound image registration algorithm toward clinical implementation for interstitial prostate brachytherapy.

Purpose: The purpose of this study was to evaluate and clinically implement a deformable surface-based magnetic resonance imaging (MRI) to three-dimensional ultrasound (US) image registration algorithm for prostate brachytherapy (BT) with the aim to reduce operator dependence and facilitate dose escalation to an MRI-defined target.

Methods And Materials: Our surface-based deformable image registration (DIR) algorithm first translates and scales to align the US- and MR-defined prostate surfaces, followed by deformation of the MR-defined prostate surface to match the US-defined prostate surface. The algorithm performance was assessed in a phantom using three deformation levels, followed by validation in three retrospective high-dose-rate BT clinical cases.

Automatic femoral articular cartilage segmentation using deep learning in three-dimensional ultrasound images of the knee.

Objective: This study aimed to develop a deep learning-based approach to automatically segment the femoral articular cartilage (FAC) in 3D ultrasound (US) images of the knee to increase time efficiency and decrease rater variability.

Design: Our method involved deep learning predictions on 2DUS slices sampled in the transverse plane to view the cartilage of the femoral trochlea, followed by reconstruction into a 3D surface. A 2D U-Net was modified and trained using a dataset of 200 2DUS images resliced from 20 3DUS images.

Effect of dataset size, image quality, and image type on deep learning-based automatic prostate segmentation in 3D ultrasound.

Three-dimensional (3D) transrectal ultrasound (TRUS) is utilized in prostate cancer diagnosis and treatment, necessitating time-consuming manual prostate segmentation. We have previously developed an automatic 3D prostate segmentation algorithm involving deep learning prediction on radially sampled 2D images followed by 3D reconstruction, trained on a large, clinically diverse dataset with variable image quality. As large clinical datasets are rare, widespread adoption of automatic segmentation could be facilitated with efficient 2D-based approaches and the development of an image quality grading method.

Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images.

Purpose: Needle-based procedures for diagnosing and treating prostate cancer, such as biopsy and brachytherapy, have incorporated three-dimensional (3D) transrectal ultrasound (TRUS) imaging to improve needle guidance. Using these images effectively typically requires the physician to manually segment the prostate to define the margins used for accurate registration, targeting, and other guidance techniques. However, manual prostate segmentation is a time-consuming and difficult intraoperative process, often occurring while the patient is under sedation (biopsy) or anesthetic (brachytherapy).

Evaluation of inter-session 3D-TRUS to 3D-TRUS image registration for repeat prostate biopsies.

To ensure accurate targeting and repeatability, 3D TRUS-guided biopsies require registration to determine coordinate transformations to (1) incorporate pre-procedure biopsy plans and (2) compensate for inter-session prostate motion and deformation between repeat biopsy sessions. We evaluated prostate surface- and image-based 3D-to-3D TRUS registration by measuring the TRE of manually marked, corresponding, intrinsic fiducials in the whole gland and peripheral zone, and also evaluated the error anisotropy. The image-based rigid and non-rigid methods yielded the best results with mean TREs of 2.

Fast prostate segmentation in 3D TRUS images based on continuity constraint using an autoregressive model.

In this article a new slice-based 3D prostate segmentation method based on a continuity constraint, implemented as an autoregressive (AR) model is described. In order to decrease the propagated segmentation error produced by the slice-based 3D segmentation method, a continuity constraint was imposed in the prostate segmentation algorithm. A 3D ultrasound image was segmented using the slice-based segmentation method.

Slice-Based Prostate Segmentation in 3D US Images Using Continuity Constraint.

In the diagnosis and therapy of prostate cancer, it is critical to measure the volume of the prostate and locate its boundary. Three-dimensional transrectal ultrasound (3D TRUS) imaging has been demonstrated to be a useful technique to perform such a task. Due to image speckle as well as low contrast in ultrasound images, segmentation of the prostate in 3D US images is challenging.

3D ultrasound analysis of carotid plaque volume and surface morphology.

Morphological characterization of carotid plaques has been used for risk stratification and evaluation of response to therapy, evaluation of new risk factors, genetic research, and for quantifying effects of new anti-atherosclerotic therapies. We developed a 3D US system that allows detailed studies of carotid plaques in 3D. Our software includes 3D reconstruction, viewing, manual and semi-automated segmentation of carotid plaques, and surface morphology analysis to be used for quantitative tracking of plaque changes.