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3D Medical Images Segmentation by Effective Use of Unlabeled Data

Khalili, Hossein | 2023

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56349 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Soleymani Baghshah, Mahdieh
  7. Abstract:
  8. Image segmentation in medical imaging, as one of the most important branches of medical image analysis, often faces the challenge of limited labeled data for application in deep learning methods. The high cost of data collection and the need for expertise in image segmentation, particularly in three-dimensional images such as MRI and CT or sequence images like CMR, have all contributed to this problem, even for popular networks like U-Net, which struggle to achieve high accuracy. As a result, research efforts have focused on semi-supervised learning approaches, weakly supervised learning, as well as multi-instance learning in medical image segmentation. Unfortunately, each of these methods has its own limitations. Some fail to achieve high accuracy close to fully supervised networks while others may only work well for a specific organ or a certain type of medical image. Therefore, the existence of a network that can achieve accuracy close to fully supervised networks with minimal labeled data is crucial. In this project, we have succeeded in introducing a network that, by harnessing the intrinsic richness of its own data, achieves a level of accuracy in medical image segmentation tasks that is close to fully supervised methods. For instance, the proposed approach utilizes shared information present in 2D slices of 3D images and employs techniques such as data augmentation using unlabeled segmentation images, aiming to effectively leverage unlabeled data. Given our aim to develop a comprehensive network, we have evaluated this network on various datasets including CHAOS and Visceral, using metrics such as Dice coefficient, FLOPs, and the number of parameters for assessment. The results of this evaluation demonstrate that, with lower complexity, this network achieves better accuracy than 3D U-Net models
  9. Keywords:
  10. Semi-Supervised Learning ; Weakly Supervised Semantic Segmentation ; Medical Images ; Three Dimentional Images ; Medical Images Segmentation ; Unlabeled Data

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