OpenPros: A Large-Scale Dataset for Limited View Prostate Ultrasound Computed Tomography

• URL: http://arxiv.org/abs/2505.12261v1
• Date: Sun, 18 May 2025 06:56:49 GMT
• Title: OpenPros: A Large-Scale Dataset for Limited View Prostate Ultrasound Computed Tomography
• Authors: Hanchen Wang, Yixuan Wu, Yinan Feng, Peng Jin, Shihang Feng, Yiming Mao, James Wiskin, Baris Turkbey, Peter A. Pinto, Bradford J. Wood, Songting Luo, Yinpeng Chen, Emad Boctor, Youzuo Lin,
• Abstract summary: Prostate cancer is one of the most common and lethal cancers among men.<n>Traditional transrectal ultrasound methods suffer from low sensitivity, especially in detecting anteriorly located tumors.<n>OpenPros is the first large-scale benchmark dataset explicitly developed for limited-view prostate USCT.
• Score: 25.844490531325537
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prostate cancer is one of the most common and lethal cancers among men, making its early detection critically important. Although ultrasound imaging offers greater accessibility and cost-effectiveness compared to MRI, traditional transrectal ultrasound methods suffer from low sensitivity, especially in detecting anteriorly located tumors. Ultrasound computed tomography provides quantitative tissue characterization, but its clinical implementation faces significant challenges, particularly under anatomically constrained limited-angle acquisition conditions specific to prostate imaging. To address these unmet needs, we introduce OpenPros, the first large-scale benchmark dataset explicitly developed for limited-view prostate USCT. Our dataset includes over 280,000 paired samples of realistic 2D speed-of-sound (SOS) phantoms and corresponding ultrasound full-waveform data, generated from anatomically accurate 3D digital prostate models derived from real clinical MRI/CT scans and ex vivo ultrasound measurements, annotated by medical experts. Simulations are conducted under clinically realistic configurations using advanced finite-difference time-domain and Runge-Kutta acoustic wave solvers, both provided as open-source components. Through comprehensive baseline experiments, we demonstrate that state-of-the-art deep learning methods surpass traditional physics-based approaches in both inference efficiency and reconstruction accuracy. Nevertheless, current deep learning models still fall short of delivering clinically acceptable high-resolution images with sufficient accuracy. By publicly releasing OpenPros, we aim to encourage the development of advanced machine learning algorithms capable of bridging this performance gap and producing clinically usable, high-resolution, and highly accurate prostate ultrasound images. The dataset is publicly accessible at https://open-pros.github.io/.

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