Ultrasound has become one of the most common imaging modalities in the past two decades in fields such as sonar, biomedical imaging, and non-destructive testing (NDT). Ultrasonic waves in NDT are used to inspect and evaluate the integrity of different parts and materials, from rockets and jet engines to critical infrastructure like pipes and wind turbines. Computer vision and image analysis are routinely used to detect and characterize flaws, cracks, and other hazardous or dangerous defects. Early detection of issues and dangers in components helps save billions of dollars annually worldwide, and prevents catastrophic impacts to life and the environment. These methods help ensure the safe operation of aircrafts, concrete structures, railways, and critical infrastructure in energy production that we rely on as a society.
Computer vision techniques in NDT have advanced quickly in the past few years. While traditional techniques remain useful, modern ultrasound devices can easily collect vast quantities of high-resolution data in a short amount of time, making them approachable by deep learning methods. For example, a continuous acoustic scan at sub-millimetric resolutions of the full circumference of a pipe that is multiple kilometers long can yield millions of images in a few hours. Such datasets may contain their own unique challenges, including extreme data imbalance, or the need for multi-task, weakly-supervised and semi-supervised learning. In addition, gaps remain between natural image-derived deep learning algorithms, and those for ultrasonic acoustic-derived images, including focused image denoising, image interpretation, uncertainty quantification, and automated system self-awareness. Research into these topics is therefore important for developing robust deep-learning applications in NDT.
In the last few years, the medical ultrasound field has witnessed the successful application of deep learning in both in 2D and 3D to enhance, identify, and significantly speed up the analysis process. However, in the field of NDT, ultrasound analysis comes with its own set of challenges. For example, the imaging media and conditions can be significantly less predictable than the human body, and therefore many medical ultrasound analysis methods cannot be applied immediately to NDT ultrasound data. Therefore, methods specifically for the NDT domain are thus of paramount importance to keep up with the increasingly complex demands in NDT.
The Ultrasound Dataset Challenge
In this workshop, we invite prominent researchers at the interface of NDT and deep learning to explore the future of the field. We propose a challenge of 3D surface mesh estimation. Individual researchers and teams familiar with deep learning for visual tasks are welcome to register. The required context for this challenge is explained alongside its accompanying dataset. This unprecedented NDT ultrasound dataset, released for the first time, contains labels and annotations which allow it to be used for multi-task, semi-supervised, and weakly-supervised deep learning techniques, among others.
Call For Paper
Topics of the papers include but are not limited to:
- Computer Vision in Ultrasound Images;
- Algorithms to Mitigate Data Imbalance;
- Semi-supervised Learning in Ultrasound Images;
- Supervised Learning in Ultrasound Images;
- Multi-task Learning in Ultrasound Images;
- Deep Learning in Volumetric images;
- Data and Performance Baseline;
- Normalization Techniques in Ultrasound Image Analysis;
- Image Classification and Segmentation;
- Spatial-temporal Feature Analysis;
The selected papers will be presented as oral or poster presentations.
Each submitted paper should be limited to eight pages, including figures and tables , and must follow the same policies and submission guidelines described in CVPR'23 Author Guidelines (https://cvpr2023.thecvf.com/Conferences/2023/AuthorGuidelines).
Papers submission is through the CMT system. In submitting a manuscript to this workshop, the authors acknowledge that no paper with substantially similar content has been submitted to another workshop or conference during the review period.
See the demo notebook for more details of the metrics
Mean Surface Distance / Average Symmetric Surface Distance
Residual Mean Square Distance