U-Net training models for efficient brain tumour segmentation on multi-modality CT and PET images

Medicine, and particularly radiology, is an area where vision systems bring significant benefits, which results in more accurate diagnoses, predictions, and treatment plans. This paper proposes a U-Net training model and simplifies the deep learning based framework for tumour detection and semantic segmentation that shapes frames for computer-aided diagnoses (CADx) and computer-aided detection (CADe) applications. The U-Net was used to segment glioma – tumour area. A training technique is proposed and convolutional neural network models capable of being trained on a dataset of fused full-size CT and PET scans – 512x512x1 – is addressed. The implemented algorithms were executed in a cloud environment, where storage was decoupled from compute (CPU and GPU). The resultant models’ performance was assessed utilizing multiple metrics. Trained models fully automatically detect tumours in a given dataset. The best scored coefficient (Dice Co-Eff) for a model is 0.8750. The paper which follows is a detailed reference with training parameters for U-Net for efficient brain tumour detection on CT and PET scans where the dataset is limited –- in this case, consisting of merely 20 patients.