An Efficient 3D Convolutional Neural Network for Dose Prediction in Cancer Radiotherapy from CT Images
<b>Introduction</b>: Cancer is a highly lethal disease with a significantly high mortality rate. One of the most commonly used methods for treatment is radiation therapy. However, cancer treatment using radiotherapy is a time-consuming process that requires significant manual work from p...
Saved in:
Main Authors: | Lam Thanh Hien, Pham Trung Hieu, Do Nang Toan |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-01-01
|
Series: | Diagnostics |
Subjects: | |
Online Access: | https://www.mdpi.com/2075-4418/15/2/177 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Synthetic Computed Tomography generation using deep-learning for female pelvic radiotherapy planning
by: Rachael Tulip, et al.
Published: (2025-01-01) -
Clinical experiences with robotic computed tomography-guided interventions: A comparison with manual technique
by: Ashwin Kumar A, et al.
Published: (2025-02-01) -
Low KV-low contrast medium dose one-stop dual source CT high pitch integrated coronary-carotid-cerebral-aortic CTA improves image quality and reduces both radiation and contrast medium doses
by: Meng Wang, et al.
Published: (2025-06-01) -
Deep learning-based synthetic CT for dosimetric monitoring of combined conventional radiotherapy and lattice boost in large lung tumors
by: Hongwei Zeng, et al.
Published: (2025-01-01) -
Acute ischemic stroke lesion segmentation in non-contrast CT images using 3D convolutional neural networks
by: A.V. Dobshik, et al.
Published: (2023-10-01)