HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY MASTER THESIS Research on application of deep learning approach in Multiclass Segmentation for Medical Images TRINH MINH NHAT nhat.vn Control Engineering and Automation Supervisor: Assoc. Van-Truong Pham Advisor’ signature School: School of Electrical and Electronic Engineering HA NOI, 9/2023 ACKNOWLEDGEMENTS I would like to take a moment to express my gratitude to the individuals who have been instrumental in shaping the trajectory of my academic journey and the com- pletion of this report. Van-Truong Pham, my mentor and guide, has been a constant source of inspiration. His dedication to my growth as a researcher and his insightful guidance have been invaluable.
His willingness to share his ex- pertise and time has been instrumental in helping me navigate through challenges and overcome obstacles. I also want to extend my heartfelt appreciation to Assoc. Thi-Thao Tran, whose contributions have left a lasting impact on my work. Though not my primary advisor, her constructive feedback, suggestions, and discussions have added depth and perspective to my research.
Her commitment to fostering a culture of learning and exploration has been immensely beneficial. In the broader context, I am deeply thankful for the unwavering support of my family. Their belief in my capabilities and their encouragement during both the highs and lows have been my driving force. Their sacrifices and unwavering faith have kept me motivated, and I am indebted to them for their constant presence in my life.
Lastly, I would like to acknowledge the academic community, my peers, and fellow researchers who have provided valuable insights and diverse perspectives that have enriched my work. The exchange of ideas and collaborative discussions have shaped my understanding and contributed to the depth of this report. As I reflect upon this journey, I am filled with gratitude for the people who have contributed to this report’s fruition. Their collective efforts have not only facilitated the completion of this project but have also nurtured my growth as a learner and researcher.
ABSTRACT In recent times, the prominence of deep learning-based techniques for medical im- age segmentation has surged. These approaches primarily revolve around innovat- ing architectural designs and refining loss functions. Conventional loss functions in this context often rely on global measures, such as Cross-Entropy and Dice Loss, or overall image intensity, yet they may fall short in addressing complexities like oc- clusion and intensity variations. In response, this study introduces an original loss function, melding both local and global image features, reformulated within the Mumford-Shah framework.
This novel approach is extended to the domain of mul- ticlass segmentation. The proposed deep convolutional neural network leverages this new loss function to facilitate end-to-end training while concurrently achiev- ing multi-class segmentation. Furthermore, motivated by the PiDiNet architecture, I propose a new Attention-PiDi-UNet architecture. This augmentation empowers the model to fuse contextual information across dense layers, efficiently capture se- mantic insights, and avert overfitting, resulting in precise segmentation outcomes.
The proposed approach is rigorously assessed across four distinct biomedical seg- mentation datasets encompassing various imaging modalities, spanning 2D to 3D dimensions, including dermoscopy, cardiac magnetic resonance, and brain mag- netic resonance. Evaluation results on datasets like Lesion Boundary Segmenta- tion, the dermoscopic dataset, automated cardiac diagnosis, and 6-month infant brain MRI Segmentation corroborate the algorithm’s superior performance com- pared to existing state-of-the-art methods. This robustly underscores the potency of our multiclass segmentation approach for diverse biomedical images. Student’s signature TABLE OF CONTENTS LIST OF FIGURES ii LIST OF TABLES iii LIST OF SYMBOLS iv CHAPTER 1.1 Motivation for Participating in Medical Image Segmentation Chal- lenges .2 Advancements in Medical Image Segmentation and Innovative Ap- proaches .1 Artificial Neural Networks .2 Deep neural network .1 Glorot and He Initialization .2 Non-Saturating Activation Functions .3 Convolution Neural Network .1 The Architecture of the Visual Cortex .4 Transposed Convolutional Layers in semantic segmentation 24 2.4 U-Net based architectures .2 Compact Dilation Convolution-based Module (CDCM) .1 Mumford-Shah Loss .2 Active Contour Method.
THE PROPOSED APPROACH 31 3.1 The ISIC-2018 Dataset .2 The PH2 Dataset .3 Automated Cardiac Diagnosis Challenge (ACDC) .4 6-month Infant Brain MRI Segmentation (iSeg) Dataset .3 Active Focal Loss Function .1 Dice similarity coefficient (DSC) .2 Intersection-over-Union .3 Recall, Precision, Sensitivity and Specificity .