2019
DOI: 10.1002/mp.13618
|View full text |Cite
|
Sign up to set email alerts
|

Dose distribution prediction in isodose feature‐preserving voxelization domain using deep convolutional neural network

Abstract: Purpose: To implement a framework for dose prediction using a deep convolutional neural network (CNN) based on the concept of isodose feature-preserving voxelization (IFPV) in simplifying the representation of the dose distribution. Methods: The concept of IFPV was introduced for concise representation of a treatment plan. IFPV is a sparse voxelization scheme that partitions the voxels into subgroups according to their geometric, anatomical, and dosimetric features. In this study a deep CNN was constructed to … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
21
3

Year Published

2020
2020
2023
2023

Publication Types

Select...
8

Relationship

1
7

Authors

Journals

citations
Cited by 36 publications
(24 citation statements)
references
References 31 publications
0
21
3
Order By: Relevance
“…Some deep-learning-based dose prediction studies have been made for cervical carcinoma. The studies used a general 3D-model-patch-training strategy with 16 pixels height matrix to train (shape of n×n×16) or directly used a 2D network for data training ( 5 , 31 , 32 ). From some dose prediction studies proposed, 2D network training is good enough to provide excellent results of dose prediction.…”
Section: Discussionmentioning
confidence: 99%
“…Some deep-learning-based dose prediction studies have been made for cervical carcinoma. The studies used a general 3D-model-patch-training strategy with 16 pixels height matrix to train (shape of n×n×16) or directly used a 2D network for data training ( 5 , 31 , 32 ). From some dose prediction studies proposed, 2D network training is good enough to provide excellent results of dose prediction.…”
Section: Discussionmentioning
confidence: 99%
“… 55 Similar experience in predicting dose distribution for prostate cancer was validated in a plan for 80 prostate cancer patients. 56 Although the current clinically available ML-based automatic plan effectively saved time, the generated plan still had to be corrected manually. In the future, DL-based AP commercial software is expected to generate plans that can directly meet the needs of clinical treatment.…”
Section: Methodsmentioning
confidence: 99%
“…The first part of predicting dose-related quantities has been abundantly addressed in previous literature. Regarding spatial dose prediction, two-or three-dimensional convolutional neural networks in various architectures, such as U-nets, have been widely used [9][10][11][12][13] and extended to generative models such as generative adversarial networks [14,15], although other methods such as random forests have also been studied [16]. For prediction of DVHs or other dose statistics, while overlap volume histograms evaluated on the input image have been traditionally used for this purpose [17][18][19][20][21][22][23][24], more recent literature also includes the use of neural networkbased methods to simultaneously predict spatial dose and DVHs directly from input images [25,26].…”
Section: Accepted Article 1 Introductionmentioning
confidence: 99%