A convolutional neural network-based auto-segmentation pipeline for breast cancer imaging

Medical imaging is crucial for the detection and diagnosis of breast cancer. Artificial intelligence and computer vision have rapidly become popular in medical image analyses thanks to technological advancements. To improve the effectiveness and efficiency of medical diagnosis and treatment, significant efforts have been made in the literature on medical image processing, segmentation, volumetric analysis, and prediction. This paper is interested in the development of a prediction pipeline for breast cancer studies based on 3D computed tomography (CT) scans. Several algorithms were designed and integrated to classify the suitability of the CT slices. The selected slices from patients were then further processed in the pipeline. This was followed by data generalization and volume segmentation to reduce the computation complexity. The selected input data were fed into a 3D U-Net architecture in the pipeline for analysis and volumetric predictions of cancer tumors. Three types of U-Net models were designed and compared. The experimental results show that Model 1 of U-Net obtained the highest accuracy at 91.44% with the highest memory usage; Model 2 had the lowest memory usage with the lowest accuracy at 85.18%; and Model 3 achieved a balanced performance in accuracy and memory usage, which is a more suitable configuration for the developed pipeline

3D U-Net for automatic segmentation of breast tumours

In recent years, a convergence of multitude of factors such as increasing demand for medical imaging studies, Coronavirus pandemic and general shift in interest has led to an exacerbating global shortage of radiologists. According to the World Health Organization (WHO), 22% of the world population will be over 60 years of age and in general, older people requires more medical imaging treatment. With this problem, countries around the world are looking into solutions to tackle this shortage and one such solution is the use of Artificial Intelligence. Computer Vision in the Medical Industry is increasingly becoming popular as technological advancement such as improvement in Graphics Processing Unit (GPU) has enabled more computing intensive Computer Vision Model to be trained. In this report, a 3-Dimensional (3D) Convolutional Neural Network was developed to perform image segmentation of tumour in the breast. 3D Computed Tomography Scans (CT-Scans) were utilized as training data. Firstly, data pre-processing techniques for the 3D CT-Scans were explored such as dividing volumes into smaller segments for memory optimization, normalization of 3D volumes and analysing depth of 3D CT-Scans. Thereafter, 3D U-Net architecture was explored with a focus on the design of a suitable loss function. A Tversky Cross Entropy loss function was explored to tackle the issue of data imbalance due to excessive background pixels. Lastly, model training and prediction pipeline was discussed with the prediction result of three models discussed. A hybrid model with optimized memory usage during model training was developed, with Dice Score of 89.84% obtained.Bachelor of Engineering (Aerospace Engineering