Update README.md

README.md

@@ -91,9 +91,9 @@ Each dataset (**STHELAR_40x** or **STHELAR_20x**) consists of:
 * **tissue**: Tissue type, provided as categorical labels (e.g., Breast, Lung, Colon).
 * **image**: RGB color images of size 256×256 pixels, extracted from H&E-stained whole-slide images at 40x or 20x magnification, with a 64-pixel overlap between adjacent patches.
 * **cell_id_map**: Sparse segmentation mask (CSR matrix stored as `.npz` bytes) aligned with the H&E patch, where each nucleus pixel stores its biological `cell_id` integer (`0` = background). This is a cell-identity mask, not an 'instance index' mask: the integer values are stable per slide and are designed to join with the per-slide metadata parquets below.
-* **Dice**: Dice similarity coefficient measuring the overlap between the provided segmentation masks and segmentation predicted by a pre-trained CellViT model (SAM-H encoder).
-* **Jaccard**: Jaccard index measuring segmentation accuracy relative to predictions made by a pre-trained CellViT model (SAM-H encoder).
-* **bPQ**: Binary Panoptic Quality score, evaluating segmentation and instance-detection accuracy simultaneously, computed relative to predictions made by a pre-trained CellViT model (SAM-H encoder).
+* **Dice**: Dice similarity coefficient comparing the provided segmentation mask with the segmentation mask predicted by the pre-trained CellViT model (SAM-H encoder).
+* **Jaccard**: Jaccard index comparing the provided segmentation mask with the segmentation mask predicted by the pre-trained CellViT model (SAM-H encoder).
+* **bPQ**: Binary Panoptic Quality score comparing the provided segmentation mask with the segmentation mask predicted by the pre-trained CellViT model (SAM-H encoder).
 
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