Feasibility of automated pancreas segmentation based on dynamic MRI

Gou, Shuiping; Wu, Jiaji; Liu, F; Lee, P; Rapacchi, Stanislas; Hu, Pin-Hao; Sheng, Ke

doi:10.1259/bjr.20140248

Cited by 11 publications

(10 citation statements)

References 39 publications

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“…A previous study found that Dice coefficients between computer models of pancreas segmentation and manual segmentation by radiologist were agreeable across the head, body, and tail of the pancreas 19 . Another study demonstrated reproducibility in a hybrid gradient, region growth and shape constraint segmentation method across multiple subjects 20 . Pancreas segmentation using machine learning is an area of active research and will be useful for screening large imaging datasets.…”

Section: Discussionmentioning

confidence: 97%

Repeatability and Reproducibility of Pancreas Volume Measurements Using MRI

Williams

Hilmes

Archer

et al. 2020

Sci Rep

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Reduced pancreas volume, as measured by non-contrast magnetic resonance imaging (MRi), is observed in individuals with newly-diagnosed type 1 diabetes (T1D) and declines over the first year after diagnosis. in this study, we determined the repeatability and inter-reader reproducibility of pancreas volume measurements by MRI. Test-retest scans in individuals with or without T1D (n = 16) had an intraclass correlation coefficient (ICC) of 0.985 (95% CI 0.961 to 0.995) for pancreas volume. Independent pancreas outlines by two board-certified radiologists (n = 30) yielded an ICC of 0.945 (95% CI 0.889 to 0.973). The mean Dice coefficient, a measurement of the degree of overlap between pancreas regions of interest between the two readers, was 0.77. Prandial state did not influence pancreatic measurements, as stomach volume did not correlate with pancreas volume. these data demonstrate that MRi measurements of pancreas volume between two readers are repeatable and reproducible with ICCs that correspond to excellent clinical significance (ICC > 0.9), are not related to changes in stomach volume, and could be a useful tool for clinical investigation of diabetes and other pancreas pathologies.

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Section: Discussionmentioning

confidence: 97%

Repeatability and Reproducibility of Pancreas Volume Measurements Using MRI

Williams

Hilmes

Archer

et al. 2020

Sci Rep

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“…Accurate automatic segmentation of the pancreas is helpful to MR-guided radiotherapy for pancreatic cancer (9). In the current study, we proposed a computer-aided pancreas segmentation method based on the software platform MITK with the traditional segmentation algorithm (a threshold method and a morphological method) which is simple and easy for widespread use.…”

Section: Discussionmentioning

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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“…Same concerns also apply to the presented method, as it is sensitive to tumor volume segmentation errors that are prominent with less conspicuous tumors. With recent developments of deep learning, several automated tumor segmentation studies have shown promising results [32,33]. While accurate pancreatic tumor delineation leads to ongoing research, semi-automated or automated segmentation tools may be integrated into our pipeline to improve consistency and accuracy.…”

Section: Discussionmentioning

confidence: 99%

Combined morphologic and metabolic pipeline for Positron emission tomography/computed tomography based radiotherapy response evaluation in locally advanced pancreatic adenocarcinoma

Lao

David

Torosian

et al. 2019

Physics and Imaging in Radiation Oncology

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Background and purpose: Adaptive radiation planning for pancreatic adenocarcinoma (PA) relies on accurate treatment response assessment, while traditional response evaluation criteria inefficiently characterize tumors with complex morphological features or intrinsically low metabolism. To better assess treatment response of PA, we quantify and compare regional morphological and metabolic features of the 3D pre-and post-radiation therapy (RT) tumor models. Materials and methods: Thirty-one PA patients with pre and post-RT Positron emission tomography/computed tomography (PET/CT) scans were evaluated. 3D meshes of pre-and post-RT tumors were generated and registered to establish vertex-wise correspondence. To assess tumor response, Mahalanobis distances (M dist | Fusion) between pre-and post-RT tumor surfaces with anatomic and metabolic fused vectors were calculated for each patient. M dist | Fusion was evaluated by overall survival (OS) prediction and survival risk classification. As a comparison, the same analyses were conducted on traditional imaging/physiological predictors, and distances measurements based on metabolic and morphological features only. Results: Among all the imaging/physiological parameters, M dist | Fusion was shown to be the best predictor of OS (HR = 0.52, p = 0.008), while other parameters failed to reach significance. Moreover, M dist | Fusion outperformed traditional morphologic and metabolic measurements in patient risk stratification, either alone (HR = 11.51, p < 0.001) or combined with age (HR = 9.04, p < 0.001). Conclusions: We introduced a PET/CT-based novel morphologic and metabolic pipeline for response evaluation in locally advanced PA. The fused M dist | Fusion outperformed traditional morphologic, metabolic, and physiological measurements in OS prediction and risk stratification. The novel fusion model may serve as a new imagingmarker to more accurately characterize the heterogeneous tumor RT response.

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Feasibility of automated pancreas segmentation based on dynamic MRI

Abstract: Using the hybrid method improves segmentation robustness of low-contrast images.

Cited by 11 publications

References 39 publications

Repeatability and Reproducibility of Pancreas Volume Measurements Using MRI

Repeatability and Reproducibility of Pancreas Volume Measurements Using MRI

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

Combined morphologic and metabolic pipeline for Positron emission tomography/computed tomography based radiotherapy response evaluation in locally advanced pancreatic adenocarcinoma

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