Update README.md

README.md

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-license: cc-by-nc-sa-4.0
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+---
+license: cc-by-nc-sa-4.0
+---
+
+# CVPR 2025 Competition: Foundation Models for 3D Biomedical Image Segmentation
+
+**Highly recommend watching the [webinar recording](https://www.youtube.com/playlist?list=PLWPTMGguY4Kh48ov6WTkAQDfKRrgXZqlh) to learn about the task settings and baseline methods.**
+
+The dataset covers five commonly used 3D biomedical image modalities: CT, MR, PET, Ultrasound, and Microscopy. All the images are from public datasets with a License for redistribution. 
+To reduce dataset size, all labeled slices are extracted and preprocessed into npz files. Each `npz` file contains
+- `imgs`: image data; shape: (D,H,W); Intensity range: [0, 255]
+- `gts`: ground truth; shape: (D,H,W);
+- `spacing`
+
+Folder structure
+
+- 3D_train_npz_all: complete training set
+- 3D_train_npz_random_10percent_16G: randomly selected 10% cases from the above training set. In other words, these cases have been included in the complete training set. Participants are allowed to use other criteria to select 10% cases as the coreset.
+- 3D_val_npz: validation set
+- 3D_val_gt: ground truth of validation set
+- CVPR25_TextSegFMData_with_class.json: text prompt for test-guided segmentation task
+
+
+
+## Interactive 3D segmentation ([Homepage](https://www.codabench.org/competitions/5263/))
+
+The training `npz` files contain three keys: `imgs`, `gts`, and `spacing`.
+The validation (and testing) `npz` files don't have `gts` keys. We provide an optional box key in the `npz` file, which is defined by the middle slice 2D bounding box and the top and bottom slice (closed interval).
+Here is a demo to load the data:
+
+```python
+npz = np.load(‘path to npz file’, allow_pickle=True)
+print(npz.keys())
+imgs = npz[‘imgs’]
+gts = npz[‘gts’] # will not be in the npz for testing cases
+boxes = npz[‘boxes’] # a list of bounding box prompts
+print(boxes[0].keys()) # dict_keys(['z_min', 'z_max', 'z_mid', 'z_mid_x_min', 'z_mid_y_min', 'z_mid_x_max', 'z_mid_y_max'])
+```
+
+Remarks:
+
+1. Box prompt is optional to use, where the corresponding DSC and NSD scores are also not used during ranking.
+
+2. Some objects don't have box prompts, such as vessels (filename contains `vessel`) and multicomponent brain lesions (filename contains `brats`), becuase the box is not a proper prompt for such targets. The evaluation script will generate a zero-mask and then algorithms can directly start with the point prompt for segmentation. 
+
+3. The provided box prompts is designed for better efficiency for annotators, which may not cover the whole object. [Here](https://github.com/JunMa11/CVPR-MedSegFMCompetition/blob/main/get_boxes.py ) is the script to generate box prompts from ground truth.
+
+
+## Text-guided segmentation ([Homepage](https://www.codabench.org/competitions/5651/))
+
+For the training set, we provide a json file with dataset-wise prompts `CVPR25_TextSegFMData_with_class.json`.
+
+In the text prompts, 
+- `'instance_label': 1` denotes the instance mask where each label corresponds to one instance (e.g., lesions). It is generated by tumor_instance = cc3d.connected_components(tumor_binary_mask>0) 
+- `'instance_label': 0` denotes the common semantic mask
+
+For the validation (and hidden testing) set, we provided a text key for each validation npz file
+
+```python
+npz = np.load(‘path to npz file’, allow_pickle=True)
+print(npz.keys())
+imgs = npz[‘imgs’]
+print(npz[‘text_prompts’])
+```
+
+
+
+