Semantic Segmentation of Computed Tomography Scan of Lungs

In medical imaging, segmentation is a crucial first step toward precise diagnosis and therapy planning. In this context, lung examination using Computed Tomography (CT) scans is extremely important for the early identification of lung diseases. However, the complexity of lung structures and the requirement for accurate delineation provide considerable problems in this domain. The traditional method of manual segmentation is time-consuming and prone to human mistake, which makes the need for automated and precise solutions even more pressing. A revolutionary advance in medical imaging has been made with the introduction of Convolutional Neural Networks (CNNs), particularly the U-Net architecture. By successfully retaining spatial information and capturing multi-scale properties, the solution obtained from u net model tackles the complex issues. By utilizing skip connections in conjunction with its encoder-decoder structure, the model is able to maintain important contextual elements even when downsampling presents challenges related to information loss. In this work, we provide an extensive analysis of the use of U-Net for CT scan lung segmentation. Our work not only demonstrates U-Net's accuracy in lung structure delineation, but it also provides a preview of the possible influence on clinical practice by automating lung segmentation. This powerful automated segmentation can facilitate more efficient treatment planning, speed up diagnosis, and open the door to more individualized patient care approaches. Our study demonstrates the effectiveness of this model with an Intersection over Union (IoU) score of 0.95 and an accuracy of 0.98.