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# CT Image Segmentation for Pulmonary Embolism Diagnosis

Pulmonary embolism is a lung condition most commonly caused by blood clots, also known as thromboembolism. By analyzing CT scans of the lungs, doctors can identify clots in the pulmonary vessels to facilitate timely treatment. The goal of this project is to assist physicians in detecting areas of pulmonary embolism using deep learning, reducing their workload and improving detection accuracy.
Pulmonary embolism is a lung condition most commonly caused by blood clots, also known as thromboembolism. By analyzing CT scans of the lungs, doctors can identify clots in the pulmonary vessels to facilitate timely treatment. The goal of this project is to assist physicians in detecting areas of pulmonary embolism using deep learning, reducing their workload and improving detection accuracy.

Both 2D CT slices (.dcm) and 3D CT volumes (.nii) can be used to identify pulmonary embolism. Segmenting the nii volumes slice-by-slice generates the dcm images, so these two formats can be interconverted. Alternatively, detecting embolism directly from 3D vascular models extracts the key information without needing full volume segmentation.

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