论文标题
将糖尿病足溃疡的临床描述转换为机器可解释的分割
Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentation
论文作者
论文摘要
糖尿病足性溃疡是严重的疾病，需要密切监测和管理。对于培训机器学习方法以自动使用溃疡，临床人员必须提供地面真相注释。在本文中，我们提出了一个新的糖尿病足溃疡数据集，即DFUC2022，这是最大的分段数据集，该数据集由临床医生手动描绘溃疡区域。我们评估是否可以通过深度学习网络来解释临床描述，还是应该使用图像处理精制轮廓。通过使用多种流行的深度学习算法提供基准结果，我们可以对DFU伤口界限的局限性进行新的见解，并报告相关问题。本文提供了一些关于基线模型的观察结果，以促进DFUC2022挑战与Miccai 2022。排行榜将按骰子得分进行排名，其中最佳基于FCN的方法为0.5708，DEEPLABV3+实现了0.6277的最佳分数。本文表明，使用精制轮廓作为地面真理可以与机器预测的结果更好地达成一致性。 DFUC2022将于2022年4月27日发布。
Diabetic foot ulcer is a severe condition that requires close monitoring and management. For training machine learning methods to auto-delineate the ulcer, clinical staff must provide ground truth annotations. In this paper, we propose a new diabetic foot ulcers dataset, namely DFUC2022, the largest segmentation dataset where ulcer regions were manually delineated by clinicians. We assess whether the clinical delineations are machine interpretable by deep learning networks or if image processing refined contour should be used. By providing benchmark results using a selection of popular deep learning algorithms, we draw new insights into the limitations of DFU wound delineation and report on the associated issues. This paper provides some observations on baseline models to facilitate DFUC2022 Challenge in conjunction with MICCAI 2022. The leaderboard will be ranked by Dice score, where the best FCN-based method is 0.5708 and DeepLabv3+ achieved the best score of 0.6277. This paper demonstrates that image processing using refined contour as ground truth can provide better agreement with machine predicted results. DFUC2022 will be released on the 27th April 2022.
