Refactored Framework and Added 3D Visualization Capabilities #3
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and changed the model to accept an input of three slices and output a one-slice segmentation
- Pass in the dataset at initialization - Ensure the tiff image suffix contains two "f"s - Seperate the cropped images from others - Moved the functionality for saving image to mlflow to a seperate util - Added an option to save images locally
- Passing in shapes instead of single dimensions.
- Added all-slice dataset for and saving utility for saving all-slices of inputs, targets, and generated predictions
- Included the ability to process the dataset that outputs all slices including:
* Selection of all-slice indices using dataset indices from the crop training dataset
* Saving images to mlflow or to a specified location
- Seperated out the functionality for retrieving whole-image metadata across all slices
versions of pytorch and cuda for the RTX 5090 GPU
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MikeLippincott
approved these changes
Oct 16, 2025
added 6 commits
October 16, 2025 17:17
add comments according to pr review
values based on the number of z-slices
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I'm going to wait until we understand the napari/nViz visualization problem better before I merge this pr. Just in case I need to change my code |
added 4 commits
October 20, 2025 13:09
three semantic segmentation masks
to circumvent 3D rendering issues
visuzlized in napari using nviz
After some discussion, I think this problem has be resolved enough to merge the pr, so I am merging it now. Thanks for the review @MikeLippincott! |
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This pr refactors the segmentation framework, updated the uv environment to accommodate the RTX 5090 GPU, and added 3D visualization capabilities. This pr also changes the training to accept three z-slices as input to the model, and output the middle segmentation mask. In future pr(s), I will update the framework to perform semantic segmentation and visualize those segmentations.