Enhancing Unsupervised Segmentation Frameworks for Volumetric Medical Images via Superpixel Segmentation and Agglomerative Clustering.
Authors
Nguyen MT,Nguyen PA,Le NH
Affiliations (3)
Affiliations (3)
- Institute of Data Science, College of Management, Taipei Medical University, New Taipei City, Taiwan.
- International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, New Taipei City, Taiwan.
- AI Research Center, Tra Vinh University.
Abstract
Medical image segmentation plays a crucial role in precise diagnosis and disease monitoring. Current state-of-the-art (SOTA) segmentation methods, such as nnUNet [1], require a large amount of human-annotated segmentation ground truths, which are time-consuming to produce. Furthermore, the quality of such ground-truth depends on clinician's level of expertise and imperfect segmentation may affect the performance of deep learning model. In this paper, we aim to develop a general segmentation framework that can generate precise segmentation results for volumetric images without requiring human-annotated ground truths or domain knowledge of human anatomy.
Topics
Unsupervised Machine LearningImage Interpretation, Computer-AssistedImaging, Three-DimensionalImage Processing, Computer-AssistedJournal Article