3D耳道左右耳标注数据

一、基于此高质量的3D耳道左右耳标注数据，训练深度学习模型，实现根据3D耳道扫描结果自动识别左耳还是右耳。该能力广泛应用于扫描后端处理、助听器设计等环节，有效避免因用户标识不清或操作失误导致的“左右戴反”问题；同时，还可作为设计流程中的智能校验模块，提升验配安全性与准确性。
二、基于此3D耳道左右耳标注数据中的耳别与解剖结构的空间分布规律，构建正常耳道形态的参考模型。通过比对新扫描数据的结构对称性、关键区域完整性，自动识别采集过程中的异常（如部分缺失、形变、错位），实现扫描质量的智能质检与失真预警，确保后续设计数据可靠可用。1. 数据来源
采用专业耳道3D扫描设备对用户耳道进行精准扫描，采集用户原始3D耳道影像数据。
2. 数据标注
用户的3D耳道影像数据采集完成后，由专业验配师基于临床经验与用户确认（如“左耳”、“右耳”），对扫描所得的3D模型进行人工耳别标注。标注过程中，还采用特定RGB色彩参数对耳道关键解剖结构进行可视化区分，并明确标识该模型对应的左右耳身份。
3. 数据处理
在此数据标注基础上，将标注结果与3D空间信息深度融合，以顶点坐标（包括顶点x坐标、顶点y坐标、顶点z坐标）和面片属性（由顶点构成的三角网格，包括面片顶点数量、面片顶点索引0、面片顶点索引1、面片顶点索引2）表达左右耳几何形态，每条数据只能存在顶点坐标（vertex）和面片属性（face）的其中之一，同时嵌入颜色标签与业务属性信息，构建包含“3D空间坐标+颜色语义标注+验配业务关联数据”的结构化训练数据集。
4. 数据应用
由此形成一条从“设备端原始影像采集”到“专家级语义标注”，再到“全链路业务数据融合”的标准化数据生产流程，为后续的3D耳道左右耳识别模型训练提供高质量、可追溯、可应用的标注数据支撑。

First: Based on this high-quality 3D ear canal left-right labeled dataset, a deep learning model can be trained to automatically identify whether a scanned 3D ear canal belongs to the left or right ear. This capability is widely applied in scan backend processing, hearing aid design and other links, effectively avoiding the "left-right reversal" problem caused by unclear user identification or operational errors; meanwhile, it can also serve as an intelligent verification module in the design process, improving the safety and accuracy of hearing aid fitting.
Second: Based on the spatial distribution laws of ear side and anatomical structures in this 3D ear canal left-right labeled dataset, a reference model of normal ear canal morphology can be constructed. By comparing the structural symmetry and key region integrity of new scan data, abnormalities during the collection process (such as partial loss, deformation and misalignment) can be automatically identified, realizing intelligent quality inspection and distortion warning for scanning quality, and ensuring the reliability and availability of subsequent design data.
1. Data Source: Professional 3D ear canal scanning equipment is used to accurately scan user ear canals, collecting the original 3D ear canal image data of users.
2. Data Annotation: After the collection of the user's 3D ear canal image data is completed, professional hearing aid fitters will manually label the ear side of the scanned 3D model after confirming with the user based on clinical experience (e.g., "left ear", "right ear"). During the annotation process, specific RGB color parameters are also used to visually distinguish key anatomical structures of the ear canal, and clearly mark the left/right ear identity corresponding to the model.
3. Data Processing: On the basis of this data annotation, the annotation results are deeply integrated with 3D spatial information. The geometric morphology of left and right ear canals is represented by vertex coordinates (including vertex x-coordinate, y-coordinate and z-coordinate) and face attributes (triangular mesh composed of vertices, including the number of face vertices, face vertex index 0, face vertex index 1 and face vertex index 2). Each dataset can only contain either vertex coordinates (vertex) or face attributes (face). At the same time, color labels and business attribute information are embedded to construct a structured training dataset containing "3D spatial coordinates + color semantic annotations + hearing aid fitting business-related data".
4. Data Application: This forms a standardized data production process ranging from "original image collection at the device end" to "expert-level semantic annotation", and then to "full-link business data integration", providing high-quality, traceable and applicable annotated data support for the subsequent training of 3D ear canal left-right recognition models.

搜集汇总

背景与挑战

背景概述

该数据集是一个包含257,984条记录的3D耳道左右耳标注数据，以xlsx格式存储，用于训练深度学习模型自动识别左耳或右耳，并支持耳道形态质量检测。数据来源于专业扫描设备，经过人工标注和结构化处理，具有高精度和业务关联性，广泛应用于助听器设计和扫描质量校验场景。