Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges
Mohammad Hesam Hesamian, Wenjing Jia, Xiangjian He, Paul Kennedy
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.
Topics & Concepts
Artificial intelligenceDeep learningComputer scienceSegmentationPipeline (software)Image segmentationHomogeneousSegmentation-based object categorizationImage (mathematics)Critical appraisalScale-space segmentationComponent (thermodynamics)Computer visionPattern recognition (psychology)Machine learningMedicineMathematicsPathologyProgramming languageAlternative medicineCombinatoricsPhysicsThermodynamicsAI in cancer detectionRadiomics and Machine Learning in Medical ImagingMedical Image Segmentation Techniques