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Deep Learning for 3D Medical Image Reconstruction in Surgical Assistance

Journal: Current Medical Imaging
Guest Editor(s): Dr. Riza Sulaiman
Co-Guest Editor(s): Dr. Bilkisu Larai Muhammad Bello,Dr.Bibhu Kalyan Mishra
Submission closes on: 24th January, 2026

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Impact Factor Current: 1.1
5 - Year: 1
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Scopus CiteScore2.6 View Details

Introduction

Deep learning, a subset of machine learning, utilizes artificial neural networks to analyze complex data, drawing inspiration from the human brain. In medical imaging, deep learning is pivotal for tasks like diagnosing diseases, classifying lesions, and predicting cancer risks. Techniques like convolutional neural networks (CNNs) enhance precision in medical image processing, overcoming challenges such as inconsistent lighting, low resolution, and radiation risks in imaging modalities like X-rays, CT scans, and MRIs. Deep learning models can analyze extensive data, uncovering patterns beyond human capability and enabling non-invasive diagnostics. However, their limitations include inflexibility post-training, lack of logic, and interpretability challenges. Preprocessing medical images to reduce artifacts and standardize data improves analysis and clinical outcomes. Applications extend to electronic health records for disease prediction and 3D medical image reconstruction for surgical assistance, transforming diagnostics and treatment precision while reducing errors and unnecessary procedures.

Keywords

Deep Learning, Medical Imaging, Artificial Neural Networks, Image Preprocessing, Convolutional Neural Networks (CNNs), Disease Prediction, Medical Image Processing, Electronic Health Records, 3D Medical Image Reconstruction, Radiation Effects

Sub-topics

  • Superior Deep Learning Frameworks for 3D Reconstruction of Medical Images.
  • Surgical 3D Image Reconstruction Using Transfer Learning and Pre-trained Models.
  • Reconstruction and Visualization of 3D Images in Real Time for Surgical Navigation.
  • Enhancing 3D Image Quality in Surgical Assistance using Generative Adversarial Networks (GANs).
  • 3D reconstruction using deep learning for image denoising and noise reduction.
  • Domain Adaptation for Reconstructing 3D Medical Images.
  • Semi-supervised and Unsupervised Learning for 3D Reconstruction of Medical Images.
  • Deep Learning for Detecting Objects and Segmenting 3D Images.
  • 3D model creation tailored to each patient for customized surgical planning.
  • Automatic Prediction of Surgical Pathways from 3D Images Using Deep Learning.
  • 3D image reconstruction has clinical applications in less invasive surgery.
  • Reconstructing 3D Images with Low-Resolution and Incomplete Data.
  • Reconstructing 3D Images for Autonomous Surgery and Surgical Robotics.
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