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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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A gentle introduction to deep learning in medical image processing.

Andreas Maier¹, Christopher Syben¹, Tobias Lasser²

¹Friedrich-Alexander-University Erlangen-Nuremberg, Germany.

Zeitschrift Fur Medizinische Physik

|January 29, 2019

Summary

This paper introduces deep learning for medical image processing, covering theory and applications like image detection and diagnosis. It also discusses current limitations and future solutions for this rapidly advancing field.

Keywords:

Computer-aided diagnosis Deep learning Image reconstruction Image registration Image segmentation Introduction Machine learning Physical simulation

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Area of Science:

Medical Imaging
Computer Science
Artificial Intelligence

Background:

Deep learning has seen significant advancements, impacting various scientific domains.
Medical image processing is a key area benefiting from deep learning's progress.

Purpose of the Study:

To provide an introduction to deep learning in medical image processing.
To review fundamental concepts and applications of deep learning in this field.

Main Methods:

Review of theoretical foundations of perceptrons and neural networks.
Discussion of deep learning applications in medical image detection, recognition, segmentation, registration, and computer-aided diagnosis.
Exploration of recent trends in physical simulation, modeling, and reconstruction.

Main Results:

Deep learning has achieved remarkable results in medical image analysis tasks.
Current deep learning approaches may neglect prior knowledge, leading to potential implausibilities.
Limitations in current deep learning methodologies are identified.

Conclusions:

Deep learning offers powerful tools for medical image processing.
Addressing limitations by incorporating prior knowledge is crucial for future advancements.
Promising future approaches aim to overcome current challenges in deep learning for medical imaging.