A convolutional neural network with transfer learning for automatic discrimination between low and high-grade synovitis: a pilot study
A convolutional neural network with transfer learning for automatic discrimination between low and high-grade synovitis: a pilot study
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DOI：
10.1007/s11739-020-02583-x
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发表时间：
2021-01-02
影响因子：
4.6
通讯作者：
Iannone, Florenzo
中科院分区：
文献类型：
--
作者：
Venerito, Vincenzo;Angelini, Orazio;Iannone, Florenzo
超声引导下滑膜组织活检（USSB）可以使炎症性关节炎患者的治疗个性化。为此，量化滑膜标本中的组织炎症对于采取适当的治疗策略至关重要。本研究旨在调查计算机视觉是否有助于区分接受USSB的患者的滑膜炎等级。我们使用了一个数据库的150个显微照片滑膜谁接受USSB的患者。对于每个苏木精和伊红（H&E）染色的载玻片，计算Krenn评分。在适当的数据预处理和微调后，采用ResNet 34卷积神经网络（CNN）上的迁移学习来区分低级别和高级别滑膜炎（Krenn评分< 5 or >= 5）。我们计算了测试阶段指标，准确度，精确度（真阳性/实际结果）和召回率（真阳性/预测结果）。使用梯度凸轮算法来突出模型用于预测的图像中的区域。我们分析了12例关节炎患者标本的显微照片。训练数据集包括n.90个图像（n.42个具有高度滑膜炎）。验证和测试数据集包括n.30（n.14例高度滑膜炎）和n.30项（n.16例高度滑膜炎）。在测试阶段，准确率为100%（精确度= 1，召回率= 1）。滑膜衬里层和衬里下层中的细胞构成是CNN预测的显著决定因素。这项研究提供了一个概念证明，即具有迁移学习的计算机视觉适用于对滑膜炎进行评分。将基于CNN的方法集成到现实生活中的患者管理中可以改善风湿病学家和病理学家之间的工作流程。
Ultrasound-guided synovial tissue biopsy (USSB) may allow personalizing the treatment for patients with inflammatory arthritis. To this end, the quantification of tissue inflammation in synovial specimens can be crucial to adopt proper therapeutic strategies. This study aimed at investigating whether computer vision may be of aid in discriminating the grade of synovitis in patients undergoing USSB. We used a database of 150 photomicrographs of synovium from patients who underwent USSB. For each hematoxylin and eosin (H&E)-stained slide, Krenn's score was calculated. After proper data pre-processing and fine-tuning, transfer learning on a ResNet34 convolutional neural network (CNN) was employed to discriminate between low and high-grade synovitis (Krenn's score < 5 or >= 5). We computed test phase metrics, accuracy, precision (true positive/actual results), and recall (true positive/predicted results). The Grad-Cam algorithm was used to highlight the regions in the image used by the model for prediction. We analyzed photomicrographs of specimens from 12 patients with arthritis. The training dataset included n.90 images (n.42 with high-grade synovitis). Validation and test datasets included n.30 (n.14 high-grade synovitis) and n.30 items (n.16 with high-grade synovitis). An accuracy of 100% (precision = 1, recall = 1) was scored in the test phase. Cellularity in the synovial lining and sublining layers was the salient determinant of CNN prediction. This study provides a proof of concept that computer vision with transfer learning is suitable for scoring synovitis. Integrating CNN-based approach into real-life patient management may improve the workflow between rheumatologists and pathologists.