| | --- |
| | license: cc-by-4.0 |
| | library_name: erdes |
| | tags: |
| | - ocular-ultrasound |
| | - medical-imaging |
| | - 3d-classification |
| | - retinal-detachment |
| | pipeline_tag: image-classification |
| | --- |
| | |
| | # VNET — Normal Vs Rd |
| |
|
| | Trained model weights for **retinal detachment classification (normal vs. RD)** using ocular ultrasound videos. |
| |
|
| | | Resource | Link | |
| | |----------|------| |
| | | Paper | [](https://arxiv.org/abs/2508.04735) | |
| | | Dataset | [](https://huggingface.co/datasets/pcvlab/erdes) [](https://zenodo.org/records/18644370) | |
| | | Checkpoints | [](https://zenodo.org/records/18821031) | |
| | | Code | [](https://github.com/OSUPCVLab/ERDES) | |
| |
|
| | ## Model Details |
| |
|
| | | Property | Value | |
| | |----------|-------| |
| | | Architecture | V-Net | |
| | | Input modality | 3D ocular ultrasound video | |
| | | Input shape | `[1, 96, 128, 128]` (C, D, H, W) | |
| | | Pooling | Global Average Pooling | |
| | | Output | Binary classification (sigmoid) | |
| |
|
| | ## Labels |
| |
|
| | | Label | Class | |
| | |-------|-------| |
| | | 0 | Normal | |
| | | 1 | Retinal Detachment | |
| |
|
| | ## Usage |
| |
|
| | ```bash |
| | pip install git+https://github.com/OSUPCVLab/ERDES.git ultralytics |
| | ``` |
| |
|
| | ```python |
| | import torch |
| | import numpy as np |
| | from huggingface_hub import hf_hub_download |
| | from safetensors.torch import load_file |
| | from ultralytics import YOLO |
| | from erdes.models.components.cls_model import VNetClassifier |
| | from erdes.data.components.utils import resize |
| | |
| | # --- 1. Load YOLO for ocular globe detection --- |
| | yolo = YOLO(hf_hub_download("pcvlab/yolov8_ocular_ultrasound_globe_detection", "yolov8_ocular_ultrasound_globe_detection.pt")) |
| | |
| | # --- 2. Crop your POCUS ultrasound video using YOLO (finds largest globe bbox across all frames) --- |
| | def crop_video(video_path, model, conf=0.8): |
| | # First pass: find the largest bounding box across all frames |
| | area_max, cropping_bbox = 0, None |
| | for frame in model.predict(video_path, stream=True, verbose=False, conf=conf): |
| | if len(frame.boxes.xywhn): |
| | bbox = frame.boxes.xywhn[0].cpu().numpy() |
| | area = bbox[2] * bbox[3] |
| | if area > area_max: |
| | area_max, cropping_bbox = area, bbox |
| | |
| | if cropping_bbox is None: |
| | raise ValueError("YOLO could not detect ocular globe in video.") |
| | |
| | # Second pass: crop every frame with the largest bbox |
| | frames = [] |
| | for frame in model.predict(video_path, stream=True, verbose=False, conf=conf): |
| | img = frame.orig_img # [H, W, C] BGR |
| | h, w, _ = img.shape |
| | x_c, y_c, bw, bh = cropping_bbox |
| | x1, y1 = int((x_c - bw/2) * w), int((y_c - bh/2) * h) |
| | x2, y2 = int((x_c + bw/2) * w), int((y_c + bh/2) * h) |
| | frames.append(img[y1:y2, x1:x2]) |
| | |
| | return np.stack(frames) # [D, H, W, C] |
| | |
| | frames = crop_video("your_video.mp4", yolo) # [D, H, W, C] |
| | |
| | # --- 3. Preprocess --- |
| | video = torch.from_numpy(frames).float() # [D, H, W, C] |
| | video = video.permute(3, 0, 1, 2) # [C, D, H, W] |
| | if video.shape[0] == 3: |
| | video = video.mean(dim=0, keepdim=True) # grayscale [1, D, H, W] |
| | video = resize((96, 128, 128))(video) / 255.0 # pad + resize + normalize |
| | video = video.unsqueeze(0) # [1, 1, 96, 128, 128] |
| | |
| | # --- 4. Load model and run inference --- |
| | model = VNetClassifier(in_channels=1, num_classes=1, pooling="avg") |
| | weights = load_file(hf_hub_download("pcvlab/vnet_normal_vs_rd", "model.safetensors")) |
| | model.load_state_dict(weights) |
| | model.eval() |
| | |
| | with torch.no_grad(): |
| | logit = model(video) |
| | prob = torch.sigmoid(logit).item() |
| | pred = int(prob > 0.5) |
| | |
| | labels = {'0': 'Normal', '1': 'Retinal Detachment'} |
| | print(f"Prediction: {labels[str(pred)]} (confidence: {prob:.3f})") |
| | ``` |
| |
|
| | ## Citation |
| |
|
| | If you use this model, please cite the ERDES paper: |
| |
|
| | ```bibtex |
| | @misc{ozkut2026erdes, |
| | title={ERDES: A Benchmark Video Dataset for Retinal Detachment and Macular Status Classification in Ocular Ultrasound}, |
| | author={Yasemin Ozkut and Pouyan Navard and Srikar Adhikari and Elaine Situ-LaCasse and Josie Acu{\~n}a and Adrienne Yarnish and Alper Yilmaz}, |
| | year={2026}, |
| | eprint={2508.04735}, |
| | archivePrefix={arXiv}, |
| | primaryClass={cs.CV}, |
| | url={https://arxiv.org/abs/2508.04735} |
| | } |
| | ``` |
| |
|
