Dr. Daisuke Furukawa Profile

Dr. Daisuke Furukawa

at Canon Inc

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Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Centerline detection and estimation of pancreatic duct from abdominal CT images

C. Hattori, D. Furukawa, F. Yamazaki, Y. Fujisawa, T. Sakaguchi

Proceedings Volume 12032, 120323Q (2022) https://doi.org/10.1117/12.2603445

KEYWORDS: Pancreas, Image segmentation, Computed tomography, Error analysis, Detection and tracking algorithms, Algorithm development, Image processing, Visualization

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Purpose: The aim of this work is to automatically detect and estimate the centerline of the pancreatic duct accurately. The proposed method uses four different algorithms for tracking the pancreatic duct in each of four type pancreatic zones. Method: The pancreatic duct was divided into 4 zones; Zone A has a clearly delineated pancreatic duct, Zone B is obscured, Zone C runs from visible segment to the pancreas’ tail and Zone D extends from head of the pancreas to the first visible point. The pancreatic duct is obscured in regions of lengths from 10-40 mm. Proposed method combines deep learning CNN for duct segmentation, followed by Dijkstra's rooting algorithm for estimation of centerline in Zones A and Zones B. In Zone C and D, the centerline was estimated using geometric information. The reference standard for the pancreatic duct was determined using non-obscured data by skilled technologists. Results: Zones A, which used a neural network method, had a success rate of 94％. In Zone B, the difference was <3mm when obscured interval was 10-40mm In Zone C and D, distance between computer estimated pancreas head and tail points and operator determined anatomical point was 10mm and 19mm, respectively. Optimal characteristic cost functions for each zone allow the natural centerline to be estimated even in obscured region. The new algorithms increased the average visible centerline length by 146% with calculation time of <40 seconds.

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