Abstract
Bright objects on a dark background, such as cells in microscopy images, can sometimes be modeled as maxima of sufficient dynamic, called h-maxima. Such a model could be sufficient to count these objects in images, provided we know the dynamic threshold that tells apart actual objects from irrelevant maxima. In this paper we introduce a neural architecture that includes a morphological pipeline counting the number of h-maxima in an image, preceded by a classical CNN which predicts the dynamic h yielding the right number of objects. This is made possible by geodesic reconstruction layers, already introduced in previous work, and a new module counting connected components. This architecture is trained end-to-end to count melanocytes in microscopy images. Its performance is close to the state of the art CNN on this dataset, with much fewer parameters (1/100) and an increased interpretability.
S. Velasco-Forero—This work was granted access to the HPC resources of IDRIS under the allocation 2023-AD011012212R2 made by GENCI.
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Notes
- 1.
The dataset is available at https://bit.ly/melanocytesTRP1.
- 2.
Code available at https://github.com/peter12398/DGMM2024-comptage-cellule.
- 3.
Convolutional layers are implemented as a double convolution with twice the same number of filters.
- 4.
Tested values: \(\alpha = 1\) and \(\beta \in \{0\} \cup 10^{\{-4,-3,-2,-1,0,1,2\}} \cup 5\times 10^{\{-4,-3,-2\}}\).
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Liu, X., Blusseau, S., Velasco-Forero, S. (2024). Counting Melanocytes with Trainable h-Maxima and Connected Component Layers. In: Brunetti, S., Frosini, A., Rinaldi, S. (eds) Discrete Geometry and Mathematical Morphology. DGMM 2024. Lecture Notes in Computer Science, vol 14605. Springer, Cham. https://doi.org/10.1007/978-3-031-57793-2_32
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