acusim: a synthetic dataset for cervicocranial acupuncture points localisation

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).

由于身高、体重、脂肪比例等个体差异因素，人体不同个体间的穴位（acupoints）位置存在差异。然而，穴位标注依赖专业人员，不仅劳动强度大且成本高昂，这限制了数据集规模与检测精度。本文提出AcuSim数据集，这是一款全新的合成数据集，用于解决针灸治疗场景下，通过自动化渲染与标注流水线从输入图像中定位人体颈颅区域穴位的任务。该数据集包含63936张RGB-D图像，以及504个合成解剖模型，每个模型均标注了174个体积式穴位，以覆盖人体解剖结构的变异性与多样性。本研究在该数据集上对卷积神经网络（CNN）进行了验证，其准确率可达99.73%；结果显示，验证集中92.86%的预测结果与专家标注数据相比，误差处于5mm阈值以内。该数据集弥补了现有数据集的局限性，可应用于穴位检测与可视化相关任务，进一步推动中医（Traditional Chinese Medicine，TCM）领域的自动化发展。

搜集汇总

背景与挑战

背景概述

AcuSim是一个用于颈椎颅区针灸点定位的合成数据集，旨在通过自动渲染和标注流程解决传统针灸点标注依赖专家、劳动密集和成本高的问题。该数据集包含63,936个RGB-D图像和504个合成解剖模型，标注了174个体积针灸点，具有多分辨率图像和结构化JSON注释，支持机器学习应用，并已验证在卷积神经网络上达到99.73%的准确率，92.86%的预测误差在5毫米以内。