Image segmentation: Optimization of Level Set Methods for biological image segmentation (LSM)

An automatic way to segment grayscale 3D images of cells.

• Why ?

To study plant morphogenesis, one need to follow the evolution of a plant during time. We can use real-time live imaging and image segmentation to reconstruct the plant development at a cell level. A first pipeline, MARS-ALT, has been develop by the RDP laboratory in collaboration with the Virtual Plants team (INRIA, Montpellier), as a part of the OpenAlea platform for plant modelling. The 3D image are assembled from the fusion of three confocal stacks, and cells are segmented using a watershed algorithm. With 3D segmented images of the same plant at different steps of development, ALT can reconstruct the lineage between cells. To improve the segmentation part of the pipeline, a new method was implement in 3 steps :

• Detect exterior shape of the organ with LSM
• Perform a watershed algorithm to have a first segmentation, given the exterior shape
• Improve segmentation by re-detecting each cell shape with LSM

From left to right : original image, contour detect by LSM, watershed segmentation, cells detect by LSM

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