update: readme text

add: dark figures

Author: MarcelRosier

panoptica/README.md

@@ -3,36 +3,38 @@
 This folder contains several Jupyter notebooks to showcase different possible use cases of the [panoptica package](https://github.com/BrainLesion/panoptica).
 The package allows to compute instance-wise segmentation quality metrics for 2D and 3D semantic- and instance segmentation maps by providing 3 core modules:
 
-1. Instance Approximator
-1. Instance Matcher
-1. Panoptic Evaluator
+1. Instance Approximator: instance approximation algorithms in panoptic segmentation evaluation. Available now: connected components algorithm.
+1. Instance Matcher: instance matching algorithm in panoptic segmentation evaluation, to align and compare predicted instances with reference instances.
+1. Panoptic Evaluator: Evaluation of panoptic segmentation performance by evaluating matched instance pairs and calculating various metrics like true positives, Dice score, IoU, and ASSD for each instance.
 
-## Use Case: Semantic Segmentation Input
+## Use Cases
 
-<img src="./spine_seg/semantic/fig.png" alt="semantic_figure" height="300"/>
+### Semantic Segmentation Input
 
-Even though for many biomedical segmentation problems, an instance-wise evaluation is highly relevant and desirable, they are still addressed as semantic segmentation problems due to lack of appropriate instance labels.
+<img src="https://github.com/BrainLesion/panoptica/blob/main/examples/spine_seg/semantic/fig_dark.png?raw=true" alt="semantic_figure" height="300"/>
+
+Although for many biomedical segmentation problems, an instance-wise evaluation is highly relevant and desirable, they are still addressed as semantic segmentation problems due to lack of appropriate instance labels.
 
 Modules [1-3] can be used to obtain panoptic metrics of matched instances based on a semantic segmentation input.
 
-[Juypter Notebook Example](example_spine_semantic.ipynb)
+[Jupyter Notebook Example](https://github.com/BrainLesion/tutorials/tree/main/panoptica/example_spine_semantic.ipynb)
 
-## Use Case: Unmatched Instances Input
+### Unmatched Instances Input
 
-<img src="./spine_seg/unmatched_instance/fig.png" alt="unmatched_instance_figure" height="300"/>
+<img src="https://github.com/BrainLesion/panoptica/blob/main/examples/spine_seg/unmatched_instance/fig_dark.png?raw=true" alt="unmatched_instance_figure" height="300"/>
 
-It is a common issue that Instance segementation outputs have good segmentations with mismatched labels.
+It is a common issue that instance segementation outputs have good segmentations with mismatched labels.
 
-For this case Modules [2-3] can be utilized to match the instances and report panoptic metrics.
+For this case modules [2-3] can be utilized to match the instances and report panoptic metrics.
 
-[Juypter Notebook Example](example_spine_unmatched_instance.ipynb)
+[Jupyter Notebook Example](https://github.com/BrainLesion/tutorials/tree/main/panoptica/example_spine_unmatched_instance.ipynb)
 
-## Use Case: Matched Instances Input
+### Matched Instances Input
 
-<img src="./spine_seg/matched_instance/fig.png" alt="matched_instance_figure" height="300"/>
+<img src="https://github.com/BrainLesion/panoptica/blob/main/examples/spine_seg/matched_instance/fig_dark.png?raw=true" alt="matched_instance_figure" height="300"/>
 
 Ideally the input data already provides matched instances.
 
-In this case Module 3 can be used to directly report panoptic metrics without requiring any internal preprocessing.
+In this case module 3 can be used to directly report panoptic metrics without requiring any internal preprocessing.
 
-[Juypter Notebook Example](example_spine_matched_instance.ipynb)
+[Jupyter Notebook Example](https://github.com/BrainLesion/tutorials/tree/main/panoptica/example_spine_matched_instance.ipynb)