Tumor size estimation and 3D model viewing using Deep Learning

Abstract

Cancer is the most deadly and dreaded disease ever encountered by mankind and tumor size plays a crucial role in determining the severity and treatment for the same. Therefore, it becomes imperative to estimate the dimensions of the associated tumor with paramount accuracy and precision so as to enable radiologists and doctors, in general, to effectively prescribe a treatment post-diagnosis. Current estimation approaches of tumor size involve the manual click and drag measurements by radiologists which are functional but prone to a lot of manual errors and redundancies. To improve the overall accuracy and efficiency of the process, the authors propose a Deep learning solution that uses DICOM scan images to determine the dimensions of the tumor. Furthermore, this solution provides a 3D representation of the tumor for clear perception and comprehension and also provides treatment suggestions that aid doctors throughout the treatment. The pipeline consists of two models namely, CNN model for detection performs with an accuracy of 97.6% and a ResUNet model to segment tumor out of the brain image with accuracy of 91.54%.