International Journal of Computer Assisted Radiology and Surgery

, Volume 5, Issue 3, pp 251–262

Seeded ND medical image segmentation by cellular automaton on GPU


    • Department of Medical ImagingNotre-Dame Hospital, CHUM
  • Nicolas Piché
    • Object Research System Inc. (ORS)
Original Article

DOI: 10.1007/s11548-009-0392-0

Cite this article as:
Kauffmann, C. & Piché, N. Int J CARS (2010) 5: 251. doi:10.1007/s11548-009-0392-0



We present a GPU-based framework to perform organ segmentation in N-dimensional (ND) medical image datasets by computation of weighted distances using the Ford–Bellman algorithm (FBA). Our GPU implementation of FBA gives an alternative and optimized solution to other graph-based segmentation techniques.


Given a number of K labelled-seeds, the segmentation algorithm evolves and segments the ND image in K objects. Each region is guaranteed to be connected to seeds with the same label. The method uses a Cellular Automata (CA) to compute multiple shortest-path-trees based on the FBA. The segmentation result is obtained by K-cuts of the graph in order to separate it in K sets. A quantitative evaluation of the method was performed by measuring renal volumes of 20 patients based on magnetic resonance angiography (MRA) acquisitions. Inter-observer reproducibility, accuracy and validity were calculated and associated computing times were recorded. In a second step, the computational performances were evaluated with different graphics hardware and compared to a CPU implementation of the method using Dijkstra’s algorithm.


The ICC for inter-observer reproducibility of renal volume measurements was 0.998 (0.997–0.999) for two radiologists and the absolute mean difference between the two readers was lower than 1.2% of averaged renal volumes. The validity analysis shows an excellent agreement of our method with the results provided by a supervised segmentation method, used as reference.


The formulation of the FBA in the form of a CA is simple, efficient and straightforward, and can be implemented in low cost vendor-independent graphics hardware. The method can efficiently be applied to perform organ segmentation and quantitative evaluation in clinical routine.


Cellular automatonGPUMedical image segmentationFord–Bellman shortest path
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