MR cholangiography 3D biliary tree automatic reconstruction system

Chen, Yuan Tsung; Wang, Ming Shi

doi:10.1016/j.compmedimag.2003.11.001

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…CAD allows physicians to gain superior visualization and understanding of both normal and abnormal findings and is particularly useful for establishing an accurate diagnosis and treatment plan. To the best of our knowledge, no well‐known ANN algorithm is available for evaluating diseases of the biliary system, although, in recent years, some trials have been initiated 9–11 . One recent study 12 developed a dynamic computer analysis model for the automated detection of common bile duct (CBD) stones using a single thick‐slab MRCP image and demonstrated that quantitative analysis seemed to yield better results with a sensitivity of 68.18%, specificity of 68.55%, and negative predictive value (NPV) of 88.3%, indicating that the system had satisfactory reliability due to its low probability for missing the presence of stones in MRCP images.…”

Section: Introductionmentioning

confidence: 99%

Efficacy of an artificial neural network algorithm based on thick‐slab magnetic resonance cholangiopancreatography images for the automated diagnosis of common bile duct stones

Hou

Park

et al. 2021

J of Gastro and Hepatol

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Background and Aim Magnetic resonance cholangiopancreatography (MRCP) can accurately diagnose common bile duct (CBD) stones but is laborious to interpret. We developed an artificial neural network (ANN) capable of automatically assisting physicians with the diagnosis of CBD stones. This study aimed to evaluate the ANN's diagnostic performance for detecting CBD stones in thick‐slab MRCP images and identify clinical factors predictive of accurate diagnosis. Methods The presence of CBD stones was confirmed via direct visualization through endoscopic retrograde cholangiopancreatography (ERCP). The absence of CBD stones was confirmed by either a negative endoscopic ultrasound accompanied by clinical improvements or negative findings on ERCP. Our base networks were constructed using state‐of‐the‐art EfficientNet‐B5 neural network models, which are widely used for image classification. Results In total, 3156 images were collected from 789 patients. Of these, 2628 images from 657 patients were used for training. An additional 1924 images from 481 patients were prospectively collected for validation. Across the entire prospective validation cohort, the ANN achieved a sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of 93.03%, 97.05%, 97.01%, 93.12%, and 95.01%, respectively. Similarly, a radiologist achieved a sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy 91.16%, 93.25%, 93.22%, 90.20%, and 91.68%, respectively. In multivariate analysis, only bile duct diameter > 10 mm (odds ratio = 2.45, 95% confidence interval [1.08–6.07], P = 0.040) was related to ANN diagnostic accuracy. Conclusion Our ANN algorithm automatically and quickly diagnoses CBD stones in thick‐slab MRCP images, therein aiding physicians with optimizing clinical practice, such as whether to perform ERCP.

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Section: Introductionmentioning

confidence: 99%

Efficacy of an artificial neural network algorithm based on thick‐slab magnetic resonance cholangiopancreatography images for the automated diagnosis of common bile duct stones

Hou

Park

et al. 2021

J of Gastro and Hepatol

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“…Covered stents that are less likely to occlude are being tested. 60 And finally, we can see the biliary tract in all its immaculate arborescence using computer-assisted reconstructions, 61,62 even at the microscopic level. 61 In closing this discussion of a technological tour de force, it is as well to remember how we started, by considering the importance of trees and other natural phenomena to human well-being.…”

Section: University In 1971 Having Graduated In Medicine Frommentioning

confidence: 99%

A tree grows in the liver, and now we can see it†

Reuben

2005

Hepatology

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“…This is achieved through the use of a larger, anisotropic structuring element in the reconstruction process, allowing a non-geodesic reconstruction to be achieved. 4. Biliary tree segmentation: The final step in the processing pipeline is to segment the ductal system itself in order to arrive at a three dimensional representation of the tree suitable for further visualisation, interrogation, and analysis.…”

Section: Greyscale Reconstructionmentioning

confidence: 99%

“…In this paper we address the effective utilisation of MRCP for the automated and semiautomated screening and assessment of the pancreato-biliary system [2][3][4] through the application of novel and well focused image processing, segmentation, and visualisation techniques. The primary goal is to present a unified preprocessing pipeline designed to facilitate the subsequent robust application of CAD techniques to the analysis of MRCP data in the identification and flagging of features of potential interest to the examining radiologist.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Pancreato-Biliary System from MRCP

Robinson

Whelan

18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05)

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We present a preprocessing and segmentation scheme designed to address the particular difficulties encountered in the analysis of magnetic resonance cholangiopancreatography (MRCP) data, as a precursor to the application of computer assisted diagnosis (CAD) techniques. MRCP generates noisy, low resolution, non-isometric data which often exhibits significant greylevel inhomogeneities. This combination of characteristics results in data volumes in which reliable segmentation and analysis are difficult to achieve. In this paper we describe a data processing approach developed to overcome these difficulties and allow for the effective application of automated CAD procedures in the analysis of the biliary tree and pancreatic duct in MRCP examinations.

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MR cholangiography 3D biliary tree automatic reconstruction system

Cited by 4 publications

References 18 publications

Efficacy of an artificial neural network algorithm based on thick‐slab magnetic resonance cholangiopancreatography images for the automated diagnosis of common bile duct stones

Efficacy of an artificial neural network algorithm based on thick‐slab magnetic resonance cholangiopancreatography images for the automated diagnosis of common bile duct stones

A tree grows in the liver, and now we can see it†

Analysis of the Pancreato-Biliary System from MRCP

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