mlsanityData to 7 Oct 2026

Automated computed tomography and magnetic resonance imaging segmentation using deep learning: a beginner's guide

arXiv 2304.05901 ↗First posted 12 Apr 2023eess.IV cs.CVcited by 3

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Abstract (arXiv)

Medical image segmentation is an increasingly popular area of research in medical imaging processing and analysis. However, many researchers who are new to the field struggle with basic concepts. This tutorial paper aims to provide an overview of the fundamental concepts of medical imaging, with a focus on Magnetic Resonance and Computerized Tomography. We will also discuss deep learning algorithms, tools, and frameworks used for segmentation tasks, and suggest best practices for method development and image analysis. Our tutorial includes sample tasks using public data, and accompanying code is available on GitHub (https://github.com/MICLab-Unicamp/Medical-ImagingTutorial). By sharing our insights gained from years of experience in the field and learning from relevant literature, we hope to assist researchers in overcoming the initial challenges they may encounter in this exciting and important area of research.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 26 of its 96 references that are in the map. First posted on arXiv 12 Apr 2023.

Builds on

26 of 96 references.

References · 26

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14 more
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Cited by year

Citing papers per year: 2023: 2; 2024: 1.

3 papers.

  1. 2024XctDiff: Reconstruction of CT Images with Consistent Anatomical Structures from a Single Radiographic Projection Imagecited by 3
  2. 2023MEDPSeg: Hierarchical polymorphic multitask learning for the segmentation of ground-glass opacities, consolidation, and pulmonary structures on computed tomographycited by 2
  3. 2023Automatic segmentation of lung findings in CT and application to Long COVIDcited by 0