Feasibility of automated 3-dimensional magnetic resonance imaging pancreas segmentation

Gou, Shuiping; Lee, Percy; Hu, Peng; Rwigema, J.C.; Sheng, Ke

doi:10.1016/j.adro.2016.05.002

Cited by 12 publications

(10 citation statements)

References 40 publications

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“…Even better results have been seen for MRI‐based segmentation of pancreas. Guo et al reported the DSC > 0.83 using state‐of‐the‐art segmentation methods for T1‐weighted MRI of pancreas cancer patients . Our group is presently developing a texture‐based automatic contour‐correction method which is intended to be integrated with the method described in this study.…”

Section: Discussionmentioning

confidence: 99%

“…Guo et al reported the DSC > 0.83 using state-of-the-art segmentation methods for T1-weighted MRI of pancreas cancer patients. 22 Our group is presently developing a texturebased automatic contour-correction method which is intended to be integrated with the method described in this study. Preliminary results show the contour-correction method is capable of achieving high-contour accuracy (DSC = 0.88 AE 0.03) with T1-weighted noncontrast MRIs.…”

Section: Discussionmentioning

confidence: 99%

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Texture‐based, automatic contour validation for online adaptive replanning: A feasibility study on abdominal organs

et al. 2019

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Purpose Evaluation of contour accuracy in radiation therapy planning requires manual interaction and is one of the most limiting bottlenecks for online replanning. This study aims to develop an automatic approach to rapidly evaluate contour quality based on image texture features to facilitate the routine practice of online adaptive replanning (OLAR). Method Fifty‐five pancreas cancer patients were selected from a clinical database of patients treated at our institution from 2011 to 2018. For each patient, the pancreas head and duodenum were contoured in five images (one fraction per week) resulting in a total of 275 CT image sets with corresponding ground‐truth contours. A second set of inaccurate contours was generated using deformable‐image‐registration‐based contour propagation. Three subregions, core, inner shell and outer shell, were generated from the contour of each organ. Texture features were extracted from each subregion and descriptive features of each subregion were identified using the image set with corresponding ground‐truth contours. A three‐level decision tree model was constructed based on texture constraints empirically determined for the three subregions. The two datasets containing ground truth and inaccurate contours were merged. Randomized threefold cross‐validation was performed and repeated three times. Results The first level of the decision tree utilizes textures derived from principal component analysis of a subset of extracted features from the core subregion (five PCs for pancreas head, seven PCs for duodenum). The second and third levels of the decision tree use gray‐level co‐occurrence matrix (GLCM)‐based cluster prominence to reject inaccurate contours. The trained model identifies accurate and inaccurate contours with an average sensitivity/specificity of 85%/91% for the pancreas head and 92%/92% for the duodenum contours. The false‐positive rate is 9% and 8% for pancreas head and duodenum, respectively. The execution time is less than 15 s using a standard desktop computer. Conclusion Quantitative image features can be used to develop a model to rapidly validate the quality of an organ contour. Our model accurately classifies unseen contours as accurate or inaccurate with high sensitivity and specificity. As auto‐segmentation continues to improve in quality and accuracy, this method may be integrated into a fully automatic pipeline for auto‐segmentation, contour‐quality evaluation and contour correction, which would replace the time‐consuming manual review process, thereby facilitating the more routine practice of OLAR.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Texture‐based, automatic contour validation for online adaptive replanning: A feasibility study on abdominal organs

et al. 2019

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“…The most straightforward approach is to derive the position information directly from the MR image. Often used in this case is autocontouring, in which the tumor or organ is automatically delineated [202][203][204][205][206][207][208][209][210]. Alternative methods include template matching, in which a template shape, for example of an organ or tumor, is located on the dynamic MR images [175,177,211,212], artificial neural networks [175,178] and nonrigid image registration [157].…”

Section: Tracking and Motion Modelingmentioning

confidence: 99%

Beyond T2 and 3T: New MRI techniques for clinicians

Knowles

Friedrich

Fischer

et al. 2019

Clinical and Translational Radiation Oncology

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“…Later, Gou et al. developed a novel dictionary learning (DL) method to segment based on T2W half-Fourier acquisition single-shot turbo spin-echo (HASTE) and T1W volumetric interpolated breath-hold examination images (14). These methods were tested on three patients and two healthy volunteers.…”

Section: Introductionmentioning

confidence: 99%

Computer-aided pancreas segmentation based on 3D GRE Dixon MRI: a feasibility study

Gong

Yang

et al. 2019

Acta Radiologica Open

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Background Pancreas segmentation is of great significance for pancreatic cancer radiotherapy positioning, pancreatic structure, and function evaluation. Purpose To investigate the feasibility of computer-aided pancreas segmentation based on optimized three-dimensional (3D) Dixon magnetic resonance imaging (MRI). Material and Methods Seventeen healthy volunteers (13 men, 4 women; mean age = 53.4 ± 13.2 years; age range = 28–76 years) underwent routine and optimized 3D gradient echo (GRE) Dixon MRI at 3.0 T. The computer-aided segmentation of the pancreas was executed by the Medical Imaging Interaction ToolKit (MITK) with the traditional segmentation algorithm pipeline (a threshold method and a morphological method) on the opposed-phase and water images of Dixon. The performances of our proposed computer segmentation method were evaluated by Dice coefficients and two-dimensional (2D)/3D visualization figures, which were compared for the opposed-phase and water images of routine and optimized Dixon sequences. Results The dice coefficients of the computer-aided pancreas segmentation were 0.633 ± 0.080 and 0.716 ± 0.033 for opposed-phase and water images of routine Dixon MRI, respectively, while they were 0.415 ± 0.143 and 0.779 ± 0.048 for the optimized Dixon MRI, respectively. The Dice index was significantly higher based on the water images of optimized Dixon than those in the other three groups (all P values < 0.001), including water images of routine Dixon MRI and both of the opposed-phase images of routine and optimized Dixon sequences. Conclusion Computer-aided pancreas segmentation based on Dixon MRI is feasible. The water images of optimized Dixon obtained the best similarity with a good stability.

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Feasibility of automated 3-dimensional magnetic resonance imaging pancreas segmentation

Cited by 12 publications

References 40 publications

Texture‐based, automatic contour validation for online adaptive replanning: A feasibility study on abdominal organs

Texture‐based, automatic contour validation for online adaptive replanning: A feasibility study on abdominal organs

Beyond T2 and 3T: New MRI techniques for clinicians

Computer-aided pancreas segmentation based on 3D GRE Dixon MRI: a feasibility study

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