Automatic Segmentation of the CT Medical Images

Computed tomography (CT) imaging for diagnosis, become a standard technique to help radiological experts in clinical diagnosis. Reliable CV models are required for the detection of anatomical structures and other regions of interest (ROI). The goals of our research are:

• To automate the process so that the large number of cases can be handled with the same accuracy i.e. the results are not affected as a result of fatigue, data overload or missing manual steps.
• To achieve fast and accurate results. Very high-speed computers are, now, available at modest costs, speeding up computer-based processing in the medical field.
• To support faster communication, wherein patient care can be extended to remote areas using information technology.

In this research, we are developing state-of-the-art computer vision models for the analysis and segmentation of the CT images. Our models can be used as tools for the ground truth extraction in the process of the data preparation for the U-Net architecture of the ANN for the medical image segmentation.