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1 change: 1 addition & 0 deletions .gitignore
Original file line number Diff line number Diff line change
Expand Up @@ -42,6 +42,7 @@ coverage.xml
*.svg
*.png
*.pdf
!docs/_static/images/*.png
*_checkpoint.ipynb


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Binary file added docs/_static/images/overall.png
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2 changes: 1 addition & 1 deletion docs/index.rst
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Expand Up @@ -8,7 +8,7 @@ multi-stain whole-slide images (WSIs). It combines prompt-based tissue masking,
alignment, and nuclei-level fine registration to deliver precise cell-level correspondence across
stains.

.. figure:: _static/images/pipeline_overview.svg
.. figure:: _static/images/overall.png
:alt: CORE pipeline overview
:align: center
:width: 100%
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25 changes: 3 additions & 22 deletions docs/quickstart.rst
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Expand Up @@ -4,14 +4,12 @@ Quick Start
The fastest way to get started is to run the end-to-end notebooks inside
the ``notebooks/`` folder. The steps below show the minimal Python API.

.. figure:: _static/images/coarse_fine_registration.svg
:alt: Coarse-to-fine registration strategy
.. figure:: _static/images/overall.png
:alt: CORE pipeline overview
:align: center
:width: 100%

**Figure 1.** CORE's two-stage registration strategy. The coarse stage performs rigid and
elastic alignment at low resolution; the fine stage refines cell-level correspondence using
nuclei point-sets and shape-aware coherent point drift (CPD).
**Figure 1.** CORE pipeline overview used as an illustrative reference in this quick start.

1. Edit ``config.py``
---------------------
Expand Down Expand Up @@ -48,14 +46,6 @@ Set at least the two WSI paths and the desired resolutions:
4. Extract tissue masks
-----------------------

.. figure:: _static/images/tissue_mask_example.svg
:alt: Prompt-based tissue masking
:align: center
:width: 95%

**Figure 2.** Prompt-based tissue segmentation. VisionAgent uses a text prompt to isolate
the tissue region, producing a binary mask that is then used to guide registration.

.. code-block:: python

from core.preprocessing.preprocessing import extract_tissue_masks
Expand Down Expand Up @@ -118,15 +108,6 @@ nuclei CSV files with ``global_x`` and ``global_y`` columns.
8. Evaluate
-----------

.. figure:: _static/images/evaluation_tre.svg
:alt: Target Registration Error before and after registration
:align: center
:width: 95%

**Figure 3.** Target Registration Error (TRE) before and after CORE registration. Blue dots
are fixed landmark positions; crosses show the corresponding moving landmarks. A good
registration brings the two sets of points into close agreement.

.. code-block:: python

from core.evaluation.evaluation import evaluate_registration_tre
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