Deep Learning-Based Framework For Tumour Detection And Semantic Segmentation

For brain tumour treatment plans, the diagnoses and predictions made by medical doctors and radiologists are dependent on medical imaging. Obtaining clinically meaningful information from various imaging modalities such as computerized tomography (CT), positron emission tomography (PET) and magnetic resonance (MR) scans is the core method in software and advanced screening utilized by radiologists. In this paper, a universal and complex framework for two parts of the dose control process: tumours detection and tumours area segmentation from medical images is introduced. The framework formed the implementation of methods to detect glioma tumour from CT and PET scans. Two deep learning pre-trained models: VGG19 and VGG19-BN were investigated and utilized to fuse CT and PET examination results. Mask R-CNN (region-based convolutional neural network) was used for tumour detection - the output of the model is bounding box coordinates for each object in the image (tumour). U-Net was used to perform semantic segmentation: segment malignant cells and tumour area. The transfer learning technique was used to increase the accuracy of models while having a limited collection of the dataset. Data augmentation methods were applied to generate and increase the number of training samples. The implemented framework can be utilized for other use cases that combine object detection and area segmentation from grayscale and RGB images, especially to shape computer-aided diagnosis (CADx) and computer-aided detection (CADe) systems in the healthcare industry to facilitate and assist doctors and medical care providers.