Update 'README.md'

README.md

@@ -15,15 +15,38 @@ This repository contains the code and dataset instructions for **3DLAND**, the f
 ## 🧠 Pipeline
 
 The lesion segmentation pipeline includes:
-1. Organ segmentation via MONAI
-2. Lesion-to-organ assignment
+1. Organ segmentation via MONAI and lesion-to-organ assignment
 3. 2D mask generation using SAM prompts
 4. 3D mask propagation using MedSAM2
 
 ## 📦 Dataset
 
-The dataset (metadata + mask annotations) is hosted at:  
-👉 [Download via Zenodo](https://zenodo.org/...) *(or your link)*
+## 📦 Dataset
+
+We curated over **6,000 contrast-enhanced abdominal CT scans** from the publicly available **DeepLesion** dataset, selecting only those studies that include visible **lesions or anomalies in abdominal organs**.
+
+To transform these raw scans into a structured, organ-aware 3D segmentation benchmark, we developed a multi-stage pipeline with both automated and expert-in-the-loop components:
+
+1. **Organ Segmentation**: We used MONAI models trained on TotalSegmentator to segment seven abdominal organs — **liver**, **kidneys**, **pancreas**, **spleen**, **stomach**, and **gallbladder**.
+1.2. **Lesion-to-Organ Assignment**: Lesions were matched to the most probable organ based on **IoU** overlap and **3D proximity**, with ambiguous cases reviewed by clinicians.
+3. **2D Lesion Mask Generation**: Using **MedSAM1**, we generated lesion masks from DeepLesion's bounding boxes. We found that shrinking the box to **70%** of its original size, along with a center point prompt, significantly improved segmentation precision.
+4. **3D Mask Propagation**: The resulting 2D masks were propagated across slices using **MedSAM2**, producing dense 3D segmentations with anatomical continuity.
+
+Each lesion in the dataset is:
+- **Annotated in 2D on the slice where the lesion is most clearly visible within the CT series**
+- **Localized in 3D across all slices where the lesion is present and discernible**
+- **Assigned to a specific abdominal organ**
+- **Each 3D segmentation mask is saved as a stack of 2D PNG slices, preserving spatial consistency across the volume**
+
+The dataset includes:
+- `Phase II/2D_lesion_mask`:2D lesion masks linked to organs
+- `Pahse III/3D_lesion_mask`: 3D lesion masks linked to organs
+- ` deeplesion_Info.csv`: CSV file of Our dataset metadata according to DeepLesion metadata
+
+
+
+> All annotations underwent clinical review on 10–20% of lesions per organ to ensure high-quality ground truth.
+
 
 ## 📄 License