Fu Roxana, Leader Joseph K, Pradeep Tejus, Shi Junli, Meng Xin, Zhang Yanchun, Pu Jiantao
Department of Ophthalmology, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
Departments of Radiology and Bioengineering, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
Med Phys. 2021 Jul;48(7):3721-3729. doi: 10.1002/mp.14907. Epub 2021 May 16.
To develop and validate a deep learning algorithm to automatically detect and segment an orbital abscess depicted on computed tomography (CT).
We retrospectively collected orbital CT scans acquired on 67 pediatric subjects with a confirmed orbital abscess in the setting of infectious orbital cellulitis. A context-aware convolutional neural network (CA-CNN) was developed and trained to automatically segment orbital abscess. To reduce the requirement for a large dataset, transfer learning was used by leveraging a pre-trained model for CT-based lung segmentation. An ophthalmologist manually delineated orbital abscesses depicted on the CT images. The classical U-Net and the CA-CNN models with and without transfer learning were trained and tested on the collected dataset using the 10-fold cross-validation method. Dice coefficient, Jaccard index, and Hausdorff distance were used as performance metrics to assess the agreement between the computerized and manual segmentations.
The context-aware U-Net with transfer learning achieved an average Dice coefficient and Jaccard index of 0.78 ± 0.12 and 0.65 ± 0.13, which were consistently higher than the classical U-Net or the context-aware U-Net without transfer learning (P < 0.01). The average differences of the abscess between the computerized results and the experts in terms of volume and Hausdorff distance were 0.10 ± 0.11 mL and 1.94 ± 1.21 mm, respectively. The context-aware U-Net detected all orbital abscess without false positives.
The deep learning solution demonstrated promising performance in detecting and segmenting orbital abscesses on CT images in strong agreement with a human observer.
开发并验证一种深度学习算法，以自动检测和分割计算机断层扫描（CT）上显示的眼眶脓肿。
我们回顾性收集了67例确诊为感染性眼眶蜂窝织炎伴眼眶脓肿的儿科患者的眼眶CT扫描图像。开发并训练了一种上下文感知卷积神经网络（CA-CNN），以自动分割眼眶脓肿。为了减少对大数据集的需求，通过利用基于CT的肺部分割的预训练模型来进行迁移学习。一名眼科医生手动勾勒出CT图像上显示的眼眶脓肿。使用10折交叉验证方法，在收集的数据集上对具有和不具有迁移学习的经典U-Net和CA-CNN模型进行训练和测试。使用Dice系数、Jaccard指数和豪斯多夫距离作为性能指标，以评估计算机分割与手动分割之间的一致性。
具有迁移学习的上下文感知U-Net的平均Dice系数和Jaccard指数分别为0.78±0.12和0.65±0.13，始终高于经典U-Net或没有迁移学习的上下文感知U-Net（P<0.01）。计算机化结果与专家在脓肿体积和豪斯多夫距离方面的平均差异分别为0.10±0.11mL和1.94±1.21mm。上下文感知U-Net检测到所有眼眶脓肿，无假阳性。
深度学习解决方案在检测和分割CT图像上的眼眶脓肿方面表现出了良好的性能，与人类观察者的结果高度一致。
