Abstract
Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation. Moreover, we summarize the most common challenges incurred and suggest possible solutions.
| Original language | English |
|---|---|
| Pages (from-to) | 582-596 |
| Number of pages | 15 |
| Journal | Journal of Digital Imaging |
| Volume | 32 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 15 Aug 2019 |
| Externally published | Yes |
Keywords
- CNN
- Deep learning
- Medical image segmentation
- Organ segmentation
ASJC Scopus subject areas
- Radiological and Ultrasound Technology
- Radiology Nuclear Medicine and imaging
- Computer Science Applications
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Hesamian, M. H., Jia, W., He, X., & Kennedy, P. (2019). Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges. Journal of Digital Imaging, 32(4), 582-596. https://doi.org/10.1007/s10278-019-00227-x