PO-1723 Deep learning method for TomoTherapy Hi-Art: prediction three‐dimensional dose distribution

The biological functions of proteins are traditionally thought to depend on well-defined three-dimensional structures, but many experimental studies have shown that disordered regions lacking fixed three-dimensional structures also have crucial biological roles. In some of these regions, disorder-order transitions are also involved in various biological processes, such as protein-protein interaction and ligand binding. Therefore, it is crucial to study disordered regions and structural transitions for further understanding of protein functions and folding. Owing to the costs and time requirements of experimental identification of natively disordered or transitional regions, the development of effective computational methods is a key research goal. In this study, we used overall residue dependencies and deep representation learning for prediction and reused the obtained disordered regions for the prediction of disorder-order transitions. Two similar and related prediction tasks were combined. Firstly, we developed a novel deep learning method, Res-BiLstm, for residue-wise disordered region prediction. Our method outperformed other predictors with respect to almost all criteria, as evaluated using an independent test set. For disorder-order transition prediction, we proposed a transfer learning method, Res-BiLstm-NN, with an acceptable but unbalanced performance, yielding reasonable results. To grasp underlining biophysical principles of disorder-order transitions, we performed qualitative analyses on the obtained results and discovered that most transitions have strong disordered or ordered preferences, and more transitions are consistent with the ordered state than the disordered state, different from conventional wisdom. To the best of our knowledge, this is the first sizable-scale study of transition prediction.

Download Full-text

Deep Learning Method for Prediction of Patient-Specific Dose Distribution in Breast Cancer

10.21203/rs.3.rs-147694/v1 ◽

2021 ◽

Author(s):

Sang Hee Ahn ◽

EunSook Kim ◽

Chankyu Kim ◽

Wonjoong Cheon ◽

Myeongsoo Kim ◽

...

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Deep Learning ◽

Dose Distribution ◽

Prediction Models ◽

The Body ◽

Patient Specific ◽

Learning Method ◽

Dose Prediction ◽

Test Sets

Abstract BackgroundPatient-specific dose prediction improves the efficiency and quality of radiation treatment planning and reduces the time required to find the optimal plan. In this study, a patient-specific dose prediction model was developed for a left-sided breast clinical case using deep learning and its performance was compared with that of conventional knowledge-based planning using RapidPlan™.MethodsPatient-specific dose prediction was performed using a contour image of the planning target volume (PTV) and organs at risk (OARs) with a U-net-based modified dose prediction neural network. A database of 50 volumetric modulated arc therapy (VMAT) plans for left-sided breast cancer patients was utilized to produce training and validation datasets. The dose prediction deep neural network (DpNet) feature weights of the previously learned convolution layers were applied to the test on a cohort of 10 test sets. With the same patient data set, dose prediction was performed for the 10 test sets after training in RapidPlan. The 3D dose distribution, absolute dose difference error, dose-volume histogram, 2D gamma index, and iso-dose dice similarity coefficient were used for quantitative evaluation of the dose prediction.ResultsThe mean absolute error (MAE) and one standard deviation (SD) between the clinical and deep learning dose prediction models were 0.02 ± 0.04%, 0.01 ± 0.83%, 0.16 ±0.82%, 0.52 ± 0.97% -0.88 ± 1.83%, -1.16 ± 2.58%, and -0.97 ± 1.73% for D95%, Dmean in the PTV, and the OARs of the body, left breast, heart, left lung, and right lung, respectively, and those measured between the clinical and RapidPlan dose prediction models were 0.02 ± 0.04%, 0.87 ± 0.63%, -0.29 ± 0.98%, 1.30 ± 0.86%, -0.32 ± 1.10%, 0.12 ± 2.13, and -1.74 ±1.79%, respectively.ConclusionsIn this study, a deep learning method for dose prediction was developed and was demonstrated to predict patient-specific doses for left-sided breast cancer accurately. Using the deep learning framework, the efficiency and accuracy of the dose prediction were compared to those of RapidPlan. The doses predicted by deep learning were superior to the results of the RapidPlan-generated VMAT plan. The proposed model will be able to maintain treatment plan quality and increase efficiency through patient dose prediction.

Download Full-text

Deep learning angiography (DLA): three-dimensional C-arm cone beam CT angiography generated from deep learning method using a convolutional neural network

Medical Imaging 2018: Physics of Medical Imaging ◽

10.1117/12.2293985 ◽

2018 ◽

Cited By ~ 1

Author(s):

Juan Montoya ◽

Yinsheng Li ◽

Charles Strother ◽

Guang-Hong Chen

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Ct Angiography ◽

Cone Beam Ct ◽

Three Dimensional ◽

Cone Beam ◽

Learning Method

Download Full-text

3D Mesh Model Classification with a Capsule Network

Algorithms ◽

10.3390/a14030099 ◽

2021 ◽

Vol 14 (3) ◽

pp. 99

Author(s):

Yang Zheng ◽

Jieyu Zhao ◽

Yu Chen ◽

Chen Tang ◽

Shushi Yu

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Traditional Method ◽

Three Dimensional ◽

Learning Method ◽

Two Dimensional ◽

3D Mesh ◽

Mesh Model ◽

3D Mesh Model ◽

Dimensional Object

With the widespread success of deep learning in the two-dimensional field, how to apply deep learning methods from two-dimensional to three-dimensional field has become a current research hotspot. Among them, the polygon mesh structure in the three-dimensional representation as a complex data structure provides an effective shape approximate representation for the three-dimensional object. Although the traditional method can extract the characteristics of the three-dimensional object through the graphical method, it cannot be applied to more complex objects. However, due to the complexity and irregularity of the mesh data, it is difficult to directly apply convolutional neural networks to 3D mesh data processing. Considering this problem, we propose a deep learning method based on a capsule network to effectively classify mesh data. We first design a polynomial convolution template. Through a sliding operation similar to a two-dimensional image convolution window, we directly sample on the grid surface, and use the window sampling surface as the minimum unit of calculation. Because a high-order polynomial can effectively represent a surface, we fit the approximate shape of the surface through the polynomial, use the polynomial parameter as the shape feature of the surface, and add the center point coordinates and normal vector of the surface as the pose feature of the surface. The feature is used as the feature vector of the surface. At the same time, to solve the problem of the introduction of a large number of pooling layers in traditional convolutional neural networks, the capsule network is introduced. For the problem of nonuniform size of the input grid model, the capsule network attitude parameter learning method is improved by sharing the weight of the attitude matrix. The amount of model parameters is reduced, and the training efficiency of the 3D mesh model is further improved. The experiment is compared with the traditional method and the latest two methods on the SHREC15 data set. Compared with the MeshNet and MeshCNN, the average recognition accuracy in the original test set is improved by 3.4% and 2.1%, and the average after fusion of features the accuracy reaches 93.8%. At the same time, under the premise of short training time, this method can also achieve considerable recognition results through experimental verification. The three-dimensional mesh classification method proposed in this paper combines the advantages of graphics and deep learning methods, and effectively improves the classification effect of 3D mesh model.

Download Full-text

Meshless physics‐informed deep learning method for three‐dimensional solid mechanics

International Journal for Numerical Methods in Engineering ◽

10.1002/nme.6828 ◽

2021 ◽

Author(s):

Diab W. Abueidda ◽

Qiyue Lu ◽

Seid Koric

Keyword(s):

Deep Learning ◽

Solid Mechanics ◽

Three Dimensional ◽

Learning Method

Download Full-text

Deep learning method for prediction of patient-specific dose distribution in breast cancer

Radiation Oncology ◽

10.1186/s13014-021-01864-9 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Sang Hee Ahn ◽

EunSook Kim ◽

Chankyu Kim ◽

Wonjoong Cheon ◽

Myeongsoo Kim ◽

...

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Deep Learning ◽

Dose Distribution ◽

Prediction Models ◽

The Body ◽

Patient Specific ◽

Learning Method ◽

Dose Prediction ◽

Test Sets

Abstract Background Patient-specific dose prediction improves the efficiency and quality of radiation treatment planning and reduces the time required to find the optimal plan. In this study, a patient-specific dose prediction model was developed for a left-sided breast clinical case using deep learning, and its performance was compared with that of conventional knowledge-based planning using RapidPlan™. Methods Patient-specific dose prediction was performed using a contour image of the planning target volume (PTV) and organs at risk (OARs) with a U-net-based modified dose prediction neural network. A database of 50 volumetric modulated arc therapy (VMAT) plans for left-sided breast cancer patients was utilized to produce training and validation datasets. The dose prediction deep neural network (DpNet) feature weights of the previously learned convolution layers were applied to the test on a cohort of 10 test sets. With the same patient data set, dose prediction was performed for the 10 test sets after training in RapidPlan. The 3D dose distribution, absolute dose difference error, dose-volume histogram, 2D gamma index, and iso-dose dice similarity coefficient were used for quantitative evaluation of the dose prediction. Results The mean absolute error (MAE) and one standard deviation (SD) between the clinical and deep learning dose prediction models were 0.02 ± 0.04%, 0.01 ± 0.83%, 0.16 ± 0.82%, 0.52 ± 0.97, − 0.88 ± 1.83%, − 1.16 ± 2.58%, and − 0.97 ± 1.73% for D95%, Dmean in the PTV, and the OARs of the body, left breast, heart, left lung, and right lung, respectively, and those measured between the clinical and RapidPlan dose prediction models were 0.02 ± 0.14%, 0.87 ± 0.63%, − 0.29 ± 0.98%, 1.30 ± 0.86%, − 0.32 ± 1.10%, 0.12 ± 2.13%, and − 1.74 ± 1.79, respectively. Conclusions In this study, a deep learning method for dose prediction was developed and was demonstrated to accurately predict patient-specific doses for left-sided breast cancer. Using the deep learning framework, the efficiency and accuracy of the dose prediction were compared to those of RapidPlan. The doses predicted by deep learning were superior to the results of the RapidPlan-generated VMAT plan.

Download Full-text