Deep recurrent neural networks for prostate cancer detection: analysis of temporal enhanced ultrasound

S Azizi, S Bayat, P Yan, A Tahmasebi… - IEEE transactions on …, 2018‏ - ieeexplore.ieee.org
Temporal enhanced ultrasound (TeUS), comprising the analysis of variations in
backscattered signals from a tissue over a sequence of ultrasound frames, has been …

Ultrasound tissue classification: a review

C Shan, T Tan, J Han, D Huang - Artificial Intelligence Review, 2021‏ - Springer
Ultrasound imaging is the most widespread medical imaging modality for creating images of
the human body in clinical practice. Tissue classification in ultrasound has been established …

ProCUSNet: Prostate Cancer Detection on B-mode Transrectal Ultrasound Using Artificial Intelligence for Targeting During Prostate Biopsies

M Rusu, H Jahanandish, S Vesal, CX Li… - European Urology …, 2025‏ - Elsevier
Background and objective To assess whether conventional brightness-mode (B-mode)
transrectal ultrasound images of the prostate reveal clinically significant cancers with the …

Computer-aided prostate cancer detection using ultrasound RF time series: in vivo feasibility study

F Imani, P Abolmaesumi, E Gibson… - IEEE transactions on …, 2015‏ - ieeexplore.ieee.org
This paper presents the results of a computer-aided intervention solution to demonstrate the
application of RF time series for characterization of prostate cancer, in vivo. Methods: We pre …

Ultrasound-based detection of prostate cancer using automatic feature selection with deep belief networks

S Azizi, F Imani, B Zhuang, A Tahmasebi… - … Image Computing and …, 2015‏ - Springer
We propose an automatic feature selection framework for analyzing temporal ultrasound
signals of prostate tissue. The framework consists of: 1) an unsupervised feature reduction …

Cross‐Layer Connection SegFormer Attention U‐Net for Efficient TRUS Image Segmentation

Y Shi, W Du, C Gao, X Li - International Journal of Imaging …, 2024‏ - Wiley Online Library
Accurately and rapidly segmenting the prostate in transrectal ultrasound (TRUS) images
remains challenging due to the complex semantic information in ultrasound images. The …

Augmenting MRI–transrectal ultrasound-guided prostate biopsy with temporal ultrasound data: a clinical feasibility study

F Imani, B Zhuang, A Tahmasebi, JT Kwak, S Xu… - International journal of …, 2015‏ - Springer
Purpose In recent years, fusion of multi-parametric MRI (mp-MRI) with transrectal ultrasound
(TRUS)-guided biopsy has enabled targeted prostate biopsy with improved cancer yield …

Classification of prostate cancer grades and t-stages based on tissue elasticity using medical image analysis

S Yang, V Jojic, J Lian, R Chen, H Zhu… - Medical Image Computing …, 2016‏ - Springer
In this paper, we study the correlation of tissue (ie prostate) elasticity with the spread and
aggression of prostate cancers. We describe an improved, in-vivo method that estimates the …

A machine learning framework for temporal enhanced ultrasound guided prostate cancer diagnostics

S Azizi - 2018‏ - open.library.ubc.ca
The ultimate diagnosis of prostate cancer involves histopathology analysis of tissue samples
obtained through prostate biopsy, guided by either transrectal ultrasound (TRUS), or fusion …

Non-rigid body mechanical property recovery from images and videos

S Yang - 2018‏ - search.proquest.com
Material property has great importance in surgical simulation and virtual reality. The
mechanical properties of the human soft tissue are critical to characterize the tissue …