INBD

Iterative Next Boundary Detection for Instance Segmentation of Tree Rings in Microscopy Images of Shrub Cross Sections

CVPR 2023. Paper link

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<img src="assets/example0.jpg" alt="Example input image and detected tree rings"/>

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Setup:

Python version: 3.7. Other versions are known to cause issues.

#setup virtualenv
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

#download dataset
python fetch_dataset.py

#download pretrained models
python fetch_pretrained_models.py

Or use GitHub Codespaces:

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Inference

#single imagefile
python main.py inference checkpoints/INBD_EH/model.pt.zip dataset/EH/inputimages/EH_0033.jpg

#list of imagefiles
python main.py inference checkpoints/INBD_EH/model.pt.zip dataset/EH/test_inputimages.txt

adding pith pixel position as argument

In Pinus taeda L. species, U-Net model do not segment the pith pixel correctly. In such species, pith can be model as a single pixel (see paper).

#single imagefile
python main.py inference checkpoints/UruDendro/model.pt.zip dataset/UruDendro4_1504/T0_B1_N32_A.png --cy 766 --cx 709

Where --cy and --cx are the y and x coordinates of the pith pixel in the image. Model checkpoints/UruDendro/model.pt.zip was trained on the UruDendro dataset, which is a dataset of Pinus taeda L. tree rings (see dataset). Image dataset/UruDendro4_1504/T0_B1_N32_A.png is an example image from the UruDendro4 dataset (see dataset). resized to 1504x1504 pixels. Image can be downloaded from link.

To run in a list of files, the input file should contain the image paths and the pith pixel coordinates.

#list of imagefiles
python main.py inference checkpoints/UruDendro/model.pt.zip dataset/UruDendro4_1504/test_inputimages.csv

Where test_inputimages.csv is a CSV file with the following format:

Code, cx, cy
T0_B1_N32_A.png, 709, 766

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Training:

#first, train the 3-class segmentation model
python main.py train segmentation           \
  dataset/EH/train_inputimages.txt          \
  dataset/EH/train_annotations.txt

#next, train the inbd network
python main.py train INBD \
  dataset/EH/train_inputimages.txt          \
  dataset/EH/train_annotations.txt          \
  --segmentationmodel=checkpoints/segmentationmodel/model.pt.zip   #adjust path

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Dataset

We introduce a new publicly available dataset: MiSCS (Microscopic Shrub Cross Sections)

The dataset and annotations can be downloaded via python fetch_dataset.py or via the following links:

• DO (Dryas octopetala)
• EH (Empetrum hermaphroditum)
• VM (Vaccinium myrtillus)

All images were acquired by Alba Anadon-Rosell. If you have ecology-related questions, please contact a.anadon at creaf.uab.cat

If you want to use this dataset for computer vision research, please cite the publication as below.

Citation

@inproceedings{INBD,
  title     = "{Iterative Next Boundary Detection for Instance Segmentation of Tree Rings in Microscopy Images of Shrub Cross Sections}",
  author    = {Alexander Gillert and Giulia Resente and Alba Anadon‐Rosell and Martin Wilmking and Uwe von Lukas},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month     = {June},
  year      = {2023},
  pages     = {14540-14548}
}

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License

License for the source code: MPL-2.0

License for the dataset: CC BY-NC-SA 4.0