DOI: 10.5061/dryad.zs7h44jkz
title: acusim: a synthetic dataset for cervicocranial acupuncture points localisation

description: The locations of acupuncture points (acupoints) differ among human
 individuals due to variations in factors such as height, weight, and fat
 proportions. However, acupoint annotation is expert-dependent,
 labour-intensive, and highly expensive, which limits the data size and
 detection accuracy. In this paper, we introduce the "AcuSim"
 dataset as a new synthetic dataset for the task of localising points on
 the human cervicocranial area from an input image using an automatic
 render and labelling pipeline during acupuncture treatment. It includes
 the creation of 63,936 RGB-D images and 504 synthetic anatomical models
 with 174 volumetric acupoints annotated, to capture the variability and
 diversity of human anatomies. The study validates a convolutional neural
 network (CNN) on the proposed dataset with an accuracy of 99.73% and shows
 that 92.86% of predictions in the validation set align within a 5mm
 threshold of margin error when compared to expert-annotated data. This
 dataset addresses the limitations of prior datasets and can be applied to
 applications of acupoint detection and visualization, further advancing
 automation in Traditional Chinese Medicine (TCM).
description: # AcuSim: A Synthetic Dataset for Cervicocranial Acupuncture Points
 Localisation Dryad DOI: https://doi.org/10.5061/dryad.zs7h44jkz ## Dataset
 Overview A multi-view acupuncture point dataset containing: * 64x64,
 128x128, 256x256, 512×512 and 1024x1024resolution RGB images *
 Corresponding JSON annotations with: * 2D/3D keypoint coordinates *
 Visibility weights (0.9-1.0 scale) * Meridian category indices *
 Visibility masks * 174 standard acupuncture points (map.txt) * Occlusion
 handling implementation ## Key Features * **Multi-view Rendering**:
 Generated using Blender 3.5 with realistic occlusion simulation *
 **Structured Annotation**: * Default initialization for occluded points
 ([0.0, 0.0, 0.5]) * Meridian category preservation for occluded points *
 Weighted visibility scoring * **ML-Ready Format**: Preconfigured PyTorch
 DataLoader implementation ## Dataset Structure ``` dataset_root/ ├──
 map.txt # Complete list of 174 acupuncture points ├── train/ │ ├──
 image/img_512/ # Training images (*.png) │ └── label/label/ # JSON
 annotations (*.json) └── test/ # [Optional] Similar structure for testing
 ``` ## Quick Start ### Dependencies ```python Python 3.10 PyTorch 2.0+
 TensorFlow 2.60 (GPU recommended) CUDA 12.4 cuDNN 9.5.0 blender 3.6.n LTS
 under python 3.10 ``` ### Basic Usage ```python from
 acuSim_dataloader_modified import AcuPointsDataset,
 create_category_encoding # Initialize dataset dataset = AcuPointsDataset(
 image_dir="path/to/images", json_dir="path/to/labels",
 target_size=(512, 512), transform=transforms.Compose([
 transforms.Resize((512, 512)), transforms.ToTensor() ]) ) # Create
 DataLoader train_loader = DataLoader( dataset, batch_size=32,
 shuffle=True, num_workers=4 ) ``` ## Advanced Features ### Key Components
 **Occlusion Handling**: * Preserves meridian category for occluded points
 * Automatic mask generation (`create_mask()`) * Weighted visibility scores
 in JSON annotations ## Code Availability | Script | Purpose | Requirements
 | | ------------------------------------------------------------------- |
 -------------------------- | ------------ | |
 [`script.py`](https://github.com/ZoeApokalypse/acuSim) | Blender rendering
 pipeline | Blender 3.5 | |
 [`xuewei5.py`](https://github.com/ZoeApokalypse/acuSim) | Visualization
 examples | Python 3.10 | |
 [`dataloader_modified.py`](https://github.com/ZoeApokalypse/acuSim) |
 PyTorch/TF data loading | TF 2.60+ | |
 [`occlusion_detection.py`](https://github.com/ZoeApokalypse/acuSim) |
 Occlusion generation | CUDA 12.4 | ## Citation Please cite our work when
 using this dataset: Sun, Qilei; Ma, Jiatao; Craig, Paul et al. (2025).
 acusim: a synthetic dataset for cervicocranial acupuncture points
 localisation [Dataset]. Dryad. https://doi.org/10.5061/dryad.zs7h44jkz