Abstract
The accessibility and potential of deep learning techniques have increased considerably over the past years. Image segmentation is one of the many fields which have seen novel implementations being developed to solve problems in the domain. U-Net is an example of a popular deep learning model designed specifically for biomedical image segmentation, initially proposed for cell segmentation. We propose a variation of the U-Net++ model, which is itself an adaptation of U-Net, and evaluate its brain tumor segmentation capabilities. The proposed approach obtained Dice Coefficient scores of 0.7192, 0.8712, and 0.7817 for the Enhancing Tumor, Whole Tumor and Tumor Core classes of the BraTS 2019 challenge Validation Dataset. The proposed approach differs from the standard U-Net++ model in a number of ways, including the loss function, number of convolutional blocks, and method of employing deep supervision. Data augmentation and post-processing techniques were also implemented and observed to substantially improve the model predictions. Thus, this article presents a novel adaptation of the U-Net++ architecture, which is both lightweight, and performs comparably with peer-reviewed work evaluated on the same data.
Highlights
B RAIN tumors may be defined as abnormal growths of cells within the brain [1]
A detailed description of each experiment is provided in the sections to follow, including a summary of all experiments conducted in this research effort
RESEARCH OUTCOMES & LIMITATIONS Reviewing the established aim and objectives of this paper, the results show that the model performs segmentation of the multiclass brain tumor segments automatically without any human intervention
Summary
B RAIN tumors may be defined as abnormal growths of cells within the brain [1]. The 2020 Statistics for Adolescents and Young Adults [2] estimate 3700 cases of brain cancer, being the most common cause of death for men in this age group (10-39 years), and second largest cause of death overall after female breast cancer. B. U-NET AND RESIDUAL U-NET Image segmentation problems present an additional layer of difficulty compared to more standard image/object recognition problems such as scene classification. U-NET AND RESIDUAL U-NET Image segmentation problems present an additional layer of difficulty compared to more standard image/object recognition problems such as scene classification In the latter problem, a model would learn to take images of scenery as input and produce one class label for the entire image. Every pixel (or voxel for 3D images) will be assigned a class This requires more complex feature extraction to be performed by a model. Due to the spatial resolution of these images, care must be taken not to encumber a network with too many parameters This is the main inspiration behind the U-Net Convolutional Neural Network (CNN) [13]
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