Liver Tumour Segmentation

The ninth most prevalent cancer in women is liver cancer, according to the most recent estimates from the worldwide cancer statistics for 2020. It's challenging to segment the liver, and segmenting the tumor from the liver makes it more challenging. Imaging procedures like magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US) are used to separate the liver and liver tumor once a sample of liver tissue has been collected. It is not desirable to segment the liver and tumor from computed abdominal CT images based on shade of gray or forms because of the overlapping intensity and variability in the position and shape of soft tissues.In order to fill this gap, our team suggested a hybrid ResUNet model that combines the ResNet and UNet models to create a more effective way for segmenting liver and tumors from CT image volumes. The segmentation of the liver and the evaluation of the region of interest (ROI) were the two overlapping models' main applications in this project. To evaluate the liver with an abdominal CT image volume, the liver is segmented.