Minor panoptica updates (mainly description)

panoptica/README.md

@@ -3,9 +3,11 @@
 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: 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. Instance 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.
+**1. Instance Approximator:** instance approximation algorithms in panoptic segmentation evaluation. Available now: connected components algorithm.
+
+**2. Instance Matcher:** instance matching algorithm in panoptic segmentation evaluation, to align and compare predicted instances with reference instances.
+
+**3. Instance 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.
 
 ![workflow_figure](https://github.com/BrainLesion/panoptica/blob/main/examples/figures/workflow.png)
 
@@ -17,7 +19,7 @@ The package allows to compute instance-wise segmentation quality metrics for 2D
 
 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.
+**Modules [1-3]** can be used to obtain panoptic metrics of matched instances based on a semantic segmentation input.
 
 [Jupyter Notebook Example](https://github.com/BrainLesion/tutorials/tree/main/panoptica/example_spine_semantic.ipynb)
 
@@ -27,7 +29,7 @@ Modules [1-3] can be used to obtain panoptic metrics of matched instances based
 
 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.
 
 [Jupyter Notebook Example](https://github.com/BrainLesion/tutorials/tree/main/panoptica/example_spine_unmatched_instance.ipynb)
 
@@ -37,7 +39,7 @@ For this case modules [2-3] can be utilized to match the instances and report pa
 
 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.
 
 [Jupyter Notebook Example](https://github.com/BrainLesion/tutorials/tree/main/panoptica/example_spine_matched_instance.ipynb)