Abstract
Multi-modal three-dimensional (3-D) image segmentation is used in many medical applications, such as disease diagnosis, treatment planning, and image-guided surgery. Although multi-modal images provide information that no single image modality alone can provide, integrating such information to be used in segmentation is a challenging task. Numerous methods have been introduced to solve the problem of multi-modal medical image segmentation in recent years. In this paper, we propose a solution for the task of brain tumor segmentation. To this end, we first introduce a method of enhancing an existing magnetic resonance imaging (MRI) dataset by generating synthetic computed tomography (CT) images. Then, we discuss a process of systematic optimization of a convolutional neural network (CNN) architecture that uses this enhanced dataset, in order to customize it for our task. Using publicly available datasets, we show that the proposed method outperforms similar existing methods.
Highlights
Vast amounts of medical images are produced each day, most are still interpreted through visual analysis on a slice-by-slice basis [1]
We show that using generated computed tomography (CT) images improves the performance of deep learning models
Note that the higher the value of structural similarity (SSIM) and peak signal-to-noise ratio (PSNR), the better the performance, while the opposite is true for mean-squared error (MSE)
Summary
Vast amounts of medical images are produced each day, most are still interpreted through visual analysis on a slice-by-slice basis [1]. This requires experience, is time consuming, expensive, prone to human error, and most importantly, is inadequate for the processing of large-scale specimens [2]. Multi-modal medical image segmentation has the potential to deliver more reliable and accurate segmentation results. Integrating such information efficiently still remains a challenge [7]
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