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README.md

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<a href="https://ai4life.eurobioimaging.eu/open-calls/">
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[preprocessed_original.tif](https://github.com/user-attachments/files/25050087/preprocessed_original.tif)<a href="https://ai4life.eurobioimaging.eu/open-calls/">
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<img src="https://github.com/ai4life-opencalls/.github/blob/main/AI4Life_banner_giraffe_nodes_OC.png?raw=true" width="70%">
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</a>
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</p>
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Inside the `notebooks` folder you will find FIJI macro and Jupyter notebooks for:
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### Step 1 : [FIJI Macro: Wood Anatomy Image Preprocessing and Segmentation](notebooks/FIJI_preprocessing.ijm)
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### Step 1 : [FIJI Macro: Wood Anatomy Image Preprocessing and Segmentation](notebooks/Wood_Anatomy-Image_Preprocessing_and_Segmentation.ijm)
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This FIJI macro performs automated preprocessing and segmentation of confocal microscopy images of wood samples stained with Safranin. The goal is to identify healthy wood regions, segment cell lumina and cell walls, and produce cell-level masks suitable for downstream quantitative analysis.
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7. **Cell wall segmentation**: Cell wall regions are derived by combining lumen and watershed segmentations. Binary wall masks are generated and refined.
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8. **Visualization outputs**: For quality control and inspection, the macro generates transparent overlays showing wood mask, lumen segmentation, cell segmentation and cell wall segmentation on the original image.
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<img width="4512" height="1708" alt="2_prepro_img" src="https://github.com/user-attachments/assets/bcc4a856-fb45-4793-8460-80a48e866863" />
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_Figure 2: Preprocessed image._
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<img width="4512" height="1708" alt="4_segmentation" src="https://github.com/user-attachments/assets/dfe3a968-d262-42bc-ba6f-a6b07253ed04" />
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_Figure 3: Binary cell lumen segmentation._
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<img width="4512" height="1708" alt="6_wall_segmentation_on_original_image" src="https://github.com/user-attachments/assets/2f746e73-3e67-4fd6-a295-870788a69c06" />
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_Figure 4: Cell wall and cell lumen borders overlaid on the original image._
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## Acknowledgements
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AI4Life has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101057970. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

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