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Found 15 papers, 0 papers with code
Deep Learning (DL)-based Automatic Segmentation of the Internal Pudendal Artery (IPA) for Reduction of Erectile Dysfunction in Definitive Radiotherapy of Localized Prostate Cancer
Conclusion: The proposed model achieved good quality IPA contours to improve uniformity of segmentation and to facilitate introduction of standardized IPA segmentation into clinical trials and practice.
Coarse-Super-Resolution-Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI with Simultaneous Motion Estimation and Super-Resolution
Four-dimensional magnetic resonance imaging (4D-MRI) is an emerging technique for tumor motion management in image-guided radiation therapy (IGRT).
Prior Guided Deep Difference Meta-Learner for Fast Adaptation to Stylized Segmentation
With adaptation based on only the first three patients, the average DSCs were improved from 78. 6, 71. 9, 63. 0, 52. 2, 46. 3 and 69. 6 to 84. 4, 77. 8, 73. 0, 77. 8, 70. 5, 68. 1, for CTVstyle1, CTVstyle2, and CTVstyle3, Parotidsuperficial, Rectumsuperior, and Rectumposterior, respectively, showing the great potential of the Priorguided DDL network for a fast and effortless adaptation to new practice styles
Performance Deterioration of Deep Learning Models after Clinical Deployment: A Case Study with Auto-segmentation for Definitive Prostate Cancer Radiotherapy
The bladder contour quality was primarily affected by using IV contrast.
Uncertainty estimations methods for a deep learning model to aid in clinical decision-making -- a clinician's perspective
Especially for smaller, single institutional datasets, it may be important to evaluate multiple estimations techniques before incorporating a model into clinical practice.
Deep Learning based Direct Segmentation Assisted by Deformable Image Registration for Cone-Beam CT based Auto-Segmentation for Adaptive Radiotherapy
We found that DL-based direct segmentation on CBCT trained with pseudo labels and without influencer volumes shows poor performance compared to DIR-based segmentation.
Region Specific Optimization (RSO)-based Deep Interactive Registration
A test time optimization (TTO) technique was proposed to further improve the DL models' performance.
Segmentation by Test-Time Optimization (TTO) for CBCT-based Adaptive Radiation Therapy
Firstly, we trained a population model with 200 patients, and then applied TTO to the remaining 39 test patients by refining the trained population model to obtain 39 individualized models.
S2MS: Self-Supervised Learning Driven Multi-Spectral CT Image Enhancement
In S2MS framework, both the input and output labels are noisy images.
A Proof-of-Concept Study of Artificial Intelligence Assisted Contour Revision
The proposed clinical workflow of AIACR is as follows given an initial contour that requires a clinicians revision, the clinician indicates where a large revision is needed, and a trained deep learning (DL) model takes this input to update the contour.
Deep Dose Plugin Towards Real-time Monte Carlo Dose Calculation Through a Deep Learning based Denoising Algorithm
As a result, the whole MC dose calculation pipeline can be finished within 0. 15 seconds, including both GPU MC dose calculation and deep learning based denoising, achieving the real time efficiency needed for some radiotherapy applications, such as online adaptive radiotherapy.
Deep Interactive Denoiser (DID) for X-Ray Computed Tomography
However, there exists two challenges regarding the DL-based denoisers: 1) a trained model typically does not generate different image candidates with different noise-resolution tradeoffs which sometimes are needed for different clinical tasks; 2) the model generalizability might be an issue when the noise level in the testing images is different from that in the training dataset.
Probabilistic self-learning framework for Low-dose CT Denoising
Despite the indispensable role of X-ray computed tomography (CT) in diagnostic medicine field, the associated ionizing radiation is still a major concern considering that it may cause genetic and cancerous diseases.
Solution for Large-Scale Hierarchical Object Detection Datasets with Incomplete Annotation and Data Imbalance
This report demonstrates our solution for the Open Images 2018 Challenge.
