MRI-Based Synthetic CT for Radiation Treatment of Prostate Cancer

Yang, Xiaofeng; Lei, Yang; Wang, T.; Patel, Pretesh; Jiang, Xiaojun; Liu, T.; Dhabaan, Anees; Shim, Hyunsuk; Mao, Hui; Jani, Ashesh B.

doi:10.1016/j.ijrobp.2018.07.086

Cited by 11 publications

(5 citation statements)

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“…In fact, the discriminative power of these features can vary considerably across different types of data. The same is true for the previously described feature‐based machine learning methods, for example, random forest classifier for tissue segmentation …”

Section: Introductionmentioning

confidence: 73%

Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation

et al. 2019

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Purpose: Reliable automated segmentation of the prostate is indispensable for image-guided prostate interventions. However, the segmentation task is challenging due to inhomogeneous intensity distributions, variation in prostate anatomy, among other problems. Manual segmentation can be time-consuming and is subject to inter-and intraobserver variation. We developed an automated deep learning-based method to address this technical challenge. Methods: We propose a three-dimensional (3D) fully convolutional networks (FCN) with deep supervision and group dilated convolution to segment the prostate on magnetic resonance imaging (MRI). In this method, a deeply supervised mechanism was introduced into a 3D FCN to effectively alleviate the common exploding or vanishing gradients problems in training deep models, which forces the update process of the hidden layer filters to favor highly discriminative features. A group dilated convolution which aggregates multiscale contextual information for dense prediction was proposed to enlarge the effective receptive field of convolutional neural networks, which improve the prediction accuracy of prostate boundary. In addition, we introduced a combined loss function including cosine and cross entropy, which measures similarity and dissimilarity between segmented and manual contours, to further improve the segmentation accuracy. Prostate volumes manually segmented by experienced physicians were used as a gold standard against which our segmentation accuracy was measured. Results: The proposed method was evaluated on an internal dataset comprising 40 T2-weighted prostate MR volumes. Our method achieved a Dice similarity coefficient (DSC) of 0.86 AE 0.04, a mean surface distance (MSD) of 1.79 AE 0.46 mm, 95% Hausdorff distance (95%HD) of 7.98 AE 2.91 mm, and absolute relative volume difference (aRVD) of 15.65 AE 10.82. A public dataset (PROMISE12) including 50 T2-weighted prostate MR volumes was also employed to evaluate our approach. Our method yielded a DSC of 0.88 AE 0.05, MSD of 1.02 AE 0.35 mm, 95% HD of 9.50 AE 5.11 mm, and aRVD of 8.93 AE 7.56. Conclusion: We developed a novel deeply supervised deep learning-based approach with a group dilated convolution to automatically segment the MRI prostate, demonstrated its clinical feasibility, and validated its accuracy against manual segmentation. The proposed technique could be a useful tool for image-guided interventions in prostate cancer.

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Section: Introductionmentioning

confidence: 73%

Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation

et al. 2019

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“…The surveyed synthetic image-aided segmentation methods are listed in Table 6. Accurate segmentation of the pelvic OARs on CT image for treatment planning is challenging due to the poor softtissue contrast [85,258]. MRI has been used to aid CT prostate delineation, but it is not as accessible as CT for radiation therapy [259,260].…”

Section: Synthetic Image-aided Segmentationmentioning

confidence: 99%

A review of deep learning based methods for medical image multi-organ segmentation

et al. 2021

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Deep learning has revolutionized image processing and achieved the-state-of-art performance in many medical image segmentation tasks. Many deep learning-based methods have been published to segment different parts of the body for different medical applications. It is necessary to summarize the current state of development for deep learning in the field of medical image segmentation. In this paper, we aim to provide a comprehensive review with a focus on multi-organ image segmentation, which is crucial for radiotherapy where the tumor and organs-at-risk need to be contoured for treatment planning. We grouped the surveyed methods into two broad categories which are 'pixel-wise classification' and 'end-to-end segmentation'. Each category was divided into subgroups according to their network design. For each type, we listed the surveyed works, highlighted important contributions and identified specific challenges. Following the detailed review, we discussed the achievements, shortcomings and future potentials of each category. To enable direct comparison, we listed the performance of the surveyed works that used thoracic and head-and-neck benchmark datasets.

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“…With the development of machine learning in recent years, novel methods have been developed such that accurate CT equivalent images can be generated from MR images (Aouadi et al 2016, Huynh et al 2016, Han 2017, 2018b, Yang et al 2018a, 2018b, Wang et al 2018. In these algorithms, a model is trained by a large number of pairs of CT and MR images, each pair of which belongs to the same patient and is well-registered with each other.…”

Section: Introductionmentioning

confidence: 99%

MRI-based attenuation correction for brain PET/MRI based on anatomic signature and machine learning

et al. 2019

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Deriving accurate attenuation maps for PET/MRI remains a challenging problem because MRI voxel intensities are not related to properties of photon attenuation and bone/air interfaces have similarly low signal. This work presents a learning-based method to derive patient-specific computed tomography (CT) maps from routine T1-weighted MRI in their native space for attenuation correction of brain PET. We developed a machine-learning-based method using a sequence of alternating random forests under the framework of an iterative refinement model. Anatomical feature selection is included in both training and predication stages to achieve optimal performance. To evaluate its accuracy, we retrospectively investigated 17 patients, each of which has been scanned by PET/CT and MR for brain. The PET images were corrected for attenuation on CT images as ground truth, as well as on pseudo CT (PCT) images generated from MR images. The PCT images showed mean average error of 66.1 ± 8.5 HU, average correlation coefficient of 0.974 ± 0.018 and average Dice similarity coefficient (DSC) larger than 0.85 for air, bone and soft tissue. The side-by-side image comparisons and joint histograms demonstrated very good agreement of PET images after correction by PCT and CT. The mean differences of voxel values in selected VOIs were less than 4%, the mean absolute difference of all active area is around 2.5%, and the mean linear correlation coefficient is 0.989 ± 0.017 between PET images corrected by CT and PCT. This work demonstrates a novel learning-based approach to automatically generate CT images from routine T1-weighted MR images based on a random forest regression with patch-based anatomical signatures to effectively capture the relationship between the CT and MR images. Reconstructed PET images using the PCT exhibit errors well below accepted test/retest reliability of PET/CT indicating high quantitative equivalence.

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MRI-Based Synthetic CT for Radiation Treatment of Prostate Cancer

Cited by 11 publications

References 0 publications

Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation

Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation

A review of deep learning based methods for medical image multi-organ segmentation

MRI-based attenuation correction for brain PET/MRI based on anatomic signature and machine learning

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