PARSE CHALLENGE 2022: PULMONARY ARTERIES SEGMENTATION USING SWIN U-NET TRANSFORMER(SWIN UNETR) AND U-NET

In this paper, we describe a deep neural network architecture based on Swin UNETR and U-Net for segmenting the pulmonary arteries from CT scans. The final segmentation masks were created using an ensemble of six models, three based on Swin UNETR and three based on 3D U-net with residual units. Using this strategy, our group scored 84.36 % on the multi-level dice. We conducted additional investigation and separated the task into three major subtasks: Task 1: Use the default hyperparameters for plain UNET segmentation and experiment with the patch size, a key hyperparameter for UNET segmentation models. Task 2 : Develop a lung segmentation model that distinguishes between the major pulmonary artery and the branches in order to precisely assess the model's performance. Task 3 : Examining the mask by extracting small patches near the branches and large patches around the major pulmonary artery. The code of our work is available on the following link: https://github.com/akansh12/parse2022