Background
Medical imaging is a vital aspect of modern health-care, with millions of images taken each year. Clinicians (cardiologists, urologists, oncologists etc.) and other health care professionals review these images in order to provide analysis and advice for diagnosis, treatment and palliation. It is a common task and activity in reviewing images to identify and delineate a segment of an image of interest. Such a segment may help to identify tumours, lesions, other regions of interest. It could also help pinpoint the location within the body of these regions of interest. Segmentation or contouring of a medical image is frequently necessary to provide patient diagnosis and prognosis, and to develop the patient care plan. Segmentation of images by radiologists is a laborious and time-consuming task which sometimes represents a significant bottleneck in providing timely patient treatment plans. Confounding this problem is the fact that significant radiologist interpretation variability can occur which can further impact patient outcomes.
Description of the invention
University of Waterloo (UW) researchers have developed a novel method for automatic image segmentation and auto-contouring which learns and improves its segmentation technique based on expert consensus segments (Atlas-based) as well as single user input.
Advantages
The advantages of this segmentation technique are as follows:
Potential applications
Although this technology was developed with medical Imaging in mind, other potential applications include:
The University of Waterloo, renowned for its innovative spirit and co-op education model, excels in cutting-edge research across diverse fields, including advanced manufacturing, artificial intelligence, sustainability, and health technologies.
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