docs: update tool name

README.md

@@ -13,7 +13,7 @@ tags:
 
 # AtlasPatch: Whole-Slide Image Tissue Segmentation
 
-Segmentation model for whole-slide image (WSI) thumbnails, built on **Segment Anything 2 (SAM2) Tiny** and finetuned only on the normalization layers. The model takes a **power-based WSI thumbnail (longest side clamped to 1024 px, internally resized to 1024×1024)** and predicts a binary tissue mask. Training used segmented thumbnails. AtlasPatch codebase (WSI preprocessing & tooling): https://github.com/AtlasAnalyticsLab/SlideProcessor
+Segmentation model for whole-slide image (WSI) thumbnails, built on **Segment Anything 2 (SAM2) Tiny** and finetuned only on the normalization layers. The model takes a **power-based WSI thumbnail at 1.25x magnification level (resized to 1024×1024)** and predicts a binary tissue mask. Training used segmented thumbnails. AtlasPatch codebase (WSI preprocessing & tooling): https://github.com/AtlasAnalyticsLab/AtlasPatch
 
 ## Quickstart
 
@@ -23,7 +23,7 @@ Install dependencies:
 pip install atlas-patch
 ```
 
-Recommended: use the same components we ship in AtlasPatch/SlideProcessor. The segmentation service will (a) load your WSI with the registered backend, (b) build a 1.25× power thumbnail, (c) resize it to 1024×1024, (d) run SAM2 with a full-frame box, and (e) return a mask aligned to the thumbnail.
+Recommended: use the same components we ship in AtlasPatch. The segmentation service will (a) load your WSI with the registered backend, (b) build a 1.25× power thumbnail, (c) resize it to 1024×1024, (d) run SAM2 with a full-frame box, and (e) return a mask aligned to the thumbnail.
 
 ```python
 import numpy as np
@@ -32,14 +32,14 @@ from pathlib import Path
 from PIL import Image
 from importlib.resources import files
 
-from slide_processor.core.config import SegmentationConfig
-from slide_processor.services.segmentation import SAM2SegmentationService
-from slide_processor.core.wsi import WSIFactory
+from atlas_patch.core.config import SegmentationConfig
+from atlas_patch.services.segmentation import SAM2SegmentationService
+from atlas_patch.core.wsi import WSIFactory
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # 1) Config: packaged SAM2 Hiera-T config; leave checkpoint_path=None to auto-download from HF.
-cfg_path = Path(files("slide_processor.configs") / "sam2.1_hiera_t.yaml")
+cfg_path = Path(files("atlas_patch.configs") / "sam2.1_hiera_t.yaml")
 seg_cfg = SegmentationConfig(
     checkpoint_path=None,          # downloads Atlas-Patch/model.pth from Hugging Face
     config_path=cfg_path,
@@ -65,7 +65,7 @@ mask_img.save("thumbnail_mask.png")
 AtlasPatch generates thumbnails at **1.25× objective power** (power-based downsampling) and then clamps the longest side to **1024 px**. Using the same helper the library uses:
 
 ```python
-from slide_processor.core.wsi import WSIFactory
+from atlas_patch.core.wsi import WSIFactory
 
 wsi = WSIFactory.load("slide.svs")
 thumb = wsi.get_thumbnail_at_power(power=1.25, interpolation="optimise")