Bi-Level 3D Reconstruction of Malignant Pleural Mesothelioma Volume From CT Images

Abstract

Abstract This paper introduces an assistive system for diagnosing malignant pleural mesothelioma (MPM), a highly aggressive cancer caused by asbestos exposure. The system assists healthcare providers in accurately determining the tumor’s volume and the appropriate dose of chemotherapy to administer. It employs a bi-level process that uses machine learning and deep learning techniques to classify CT images of lungs and then calculate the tumor’s volume. The study addresses the challenges associated with deep nets, such as the need for large and diverse datasets, hyperparameter optimization, and potential data bias. Two CNN architectures, Inception-v3 and ResNet-50, were compared in terms of their features and performance, and three hyperparameters were optimized for each model to generate a broad range of training scenarios. To estimate the amount of cancer cells to target, CT images were used to calculate the tumor’s volume. This process involved image pre-processing, cropping volume, registering and filtering images, filling holes, segmentation, and 3D reconstruction. The results demonstrate that the developed system utilizing CNN optimizations and reconstruction of 3D images from CT images can benefit the treatment of MPM patients. The system has the potential to improve the accuracy of MPM diagnosis and the effectiveness of chemotherapy, ultimately improving patient outcomes.