Update README.md

README.md

@@ -5,6 +5,7 @@ pretty_name: Breast Divider
 tags:
 - Breast
 - MRI
+- 3d
 license: cc-by-nc-sa-4.0
 task_categories:
 - image-segmentation
@@ -13,7 +14,10 @@ size_categories:
 - 10K<n<100K
 ---
 
-# [MICCAI 2025 WOMEN] BreastDivider: A Large-Scale Dataset and Model for Left–Right Breast MRI Segmentation
+# **BreastDividerV2**: A Large-Scale Dataset and Model for Left–Right Breast MRI Segmentation and Lesion Analysis
+This is v2.0.0 of our dataset! Look for v1.0.0 under tags for the original publication.
+
+## [MICCAI 2025 WOMEN] BreastDivider: A Large-Scale Dataset and Model for Left–Right Breast MRI Segmentation
 
 **Read the paper:**  [![arXiv](https://img.shields.io/badge/arXiv-2507.13830-B31B1B.svg)](https://arxiv.org/abs/2507.13830)
 
@@ -29,9 +33,7 @@ size_categories:
 
 **Breast MRI** plays a pivotal role in breast cancer detection, diagnosis, and treatment planning. However, most existing segmentation models fail to distinguish between the **left and right breasts**, limiting their usefulness in tasks such as **unilateral classification, response evaluation, or post-mastectomy follow-up**.
 
-In this work, we introduce the **first publicly available large-scale dataset with explicit left and right breast segmentation labels**, comprising **over 17,000 3D MRI scans**. Accompanying this dataset is a [**robust nnU-Net–based segmentation model**](https://huggingface.co/ykirchhoff/BreastDividerModel), trained specifically to identify and separate left and right breast regions in clinical MRI data. This resource provides a foundation for developing anatomically aware AI models and enables large-scale pretraining for downstream breast MRI tasks.
-
-**This is v2.0.0 of our dataset! Look for v1.0.0 under tags for the original publication.**
+In this work, we introduce the **first publicly available large-scale dataset with explicit left and right breast segmentation labels**, comprising **over 17,000 3D MRI scans**. Accompanying this dataset is a [**robust nnU-Net–based segmentation model**](https://huggingface.co/ykirchhoff/BreastDividerModel), trained specifically to identify and separate left and right breast regions in clinical MRI data. This resource provides a foundation for developing anatomically aware AI models and enables large-scale pretraining for downstream breast MRI tasks. Additionally, in V2 we provide over 3,000 classification targets for healthy, benign, and malignant breasts, as well as approximately 467 segmentation masks. 
 
 ---
 
@@ -58,6 +60,7 @@ In total, the dataset comprises **17,956 3D scans**. It includes a variety of co
 The **Advanced-MRI-Breast-Lesions** dataset contains T1 DCE with multiple fat-saturated phases, delayed T1, T2, and T1 non-fat saturated sequences.  
 The **Duke dataset** includes pre-operative DCE MRI at 1.5T or 3T, with fat-saturated and non-fat saturated sequences.  
 The **EA1141 dataset**, collected across 48 clinical sites, features non-contrast and post-contrast T1-weighted, T2-weighted, and DWI sequences.  
+The **ODELIA dataset** includes images across 5 clinical sites containing T2-weighted acquisition alongside a DCE acquisition with one pre-contrast phase and between 2 and 7 post-contrast phases per study.
 
 To ensure consistency and quality, we only included MRI volumes with: