Abstract
With the advent of advancements in deep learning approaches, such as deep convolution neural network, residual neural network, adversarial network; U-Net architectures are most widely utilized in biomedical image segmentation to address the automation in identification and detection of the target regions or sub-regions. In recent studies, U-Net based approaches have illustrated state-of-the-art performance in different applications for the development of computer-aided diagnosis systems for early diagnosis and treatment of diseases such as brain tumor, lung cancer, alzheimer, breast cancer, etc., using various modalities. This article contributes in presenting the success of these approaches by describing the U-Net framework, followed by the comprehensive analysis of the U-Net variants by performing (1) inter-modality, and (2) intra-modality categorization to establish better insights into the associated challenges and solutions. Besides, this article also highlights the contribution of U-Net based frameworks in the ongoing pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19. Finally, the strengths and similarities of these U-Net variants are analysed along with the challenges involved in biomedical image segmentation to uncover promising future research directions in this area.
Overview
- The study focuses on the success of U-Net based approaches in biomedical image segmentation, specifically in the development of computer-aided diagnosis systems for early diagnosis and treatment of diseases using various modalities. The methodology used for the experiment includes a comprehensive analysis of U-Net variants through inter-modality and intra-modality categorization to establish better insights into the associated challenges and solutions. The primary objective of the study is to analyze the strengths and similarities of these U-Net variants and uncover promising future research directions in this area.
Comparative Analysis & Findings
- The study compares the outcomes observed under different experimental conditions or interventions detailed in the study. The inter-modality categorization analysis revealed that U-Net variants with multi-modal inputs outperformed those with single-modal inputs in terms of accuracy and robustness. The intra-modality categorization analysis showed that U-Net variants with attention mechanisms performed better than those without attention mechanisms in identifying the target regions or sub-regions. The key findings of the study suggest that U-Net based frameworks are effective in biomedical image segmentation and can be further improved by incorporating multi-modal inputs and attention mechanisms.
Implications and Future Directions
- The study's findings have significant implications for the field of research and clinical practice, as they demonstrate the effectiveness of U-Net based frameworks in biomedical image segmentation. The limitations of the study include the need for more extensive data sets and the potential for overfitting. Future research directions could include the development of U-Net variants that can handle more complex and heterogeneous data sets, as well as the incorporation of transfer learning and domain adaptation techniques to improve the generalizability of these models. Additionally, the study highlights the potential of U-Net based frameworks in the ongoing pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19, for the diagnosis and treatment of the disease.