用32张不同光照下的图像提升过敏皮试肿块检测准确率
Improved Allergy Wheal Detection for the Skin Prick Automated Test Device
- 基于32张多光照图像，分两步：先像素级分割，再算法解析
- 在217例验证集上，32图组合比单图检测准确率显著更高
- 适合做自动化过敏诊断的医生和研发人员参考
背景：皮肤点刺试验（SPT）是诊断吸入性过敏的金标准。皮肤点刺自动化设备（SPAT）旨在提高结果一致性，每次测试拍摄32张不同光照条件下的图像，用于过敏肿块的检测与轮廓勾画，进而辅助诊断。方法：基于868名疑似吸入性过敏患者的SPAT数据，我们设计了一种自动检测与勾画肿块的方法。为此，由专家手动标注了10,416个肿块，绘制其精确边缘多边形。SPAT设备独特的32图像数据模态需定制化处理方法。本方法分为两部分：首先使用神经网络实现像素级肿块分割，随后采用可解释的算法进行肿块检测与轮廓生成。结果：在217例独立验证集上评估性能，以常规均匀光照下单张图像作为基线输入。结论：利用32张不同光照下的图像，相比单张图像，显著提升了检测准确率。
原文摘要 · Abstract (English)
Background: The skin prick test (SPT) is the gold standard for diagnosing sensitization to inhalant allergies. The Skin Prick Automated Test (SPAT) device was designed for increased consistency in test results, and captures 32 images to be jointly used for allergy wheal detection and delineation, which leads to a diagnosis. Materials and Methods: Using SPAT data from $868$ patients with suspected inhalant allergies, we designed an automated method to detect and delineate wheals on these images. To this end, $10,416$ wheals were manually annotated by drawing detailed polygons along the edges. The unique data-modality of the SPAT device, with $32$ images taken under distinct lighting conditions, requires a custom-made approach. Our proposed method consists of two parts: a neural network component that segments the wheals on the pixel level, followed by an algorithmic and interpretable approach for detecting and delineating the wheals. Results: We evaluate the performance of our method on a hold-out validation set of $217$ patients. As a baseline we use a single conventionally lighted image per SPT as input to our method. Conclusion: Using the $32$ SPAT images under various lighting conditions offers a considerably higher accuracy than a single image in conventional, uniform light.
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