论文标题
自动分割，检测心脏MRI的局部分割失败
Automatic segmentation with detection of local segmentation failures in cardiac MRI
论文作者
论文摘要
心脏磁共振图像（CMRI）中心脏解剖结构的分割是自动诊断和心血管疾病预后的先决条件。为了提高分割方法的鲁棒性和性能，本研究结合了CMRI中分割不确定性的自动分割和评估，以检测包含局部分割失败的图像区域。对三个最先进的卷积神经网络（CNN）进行了训练，可以自动分割心脏解剖结构，并获得两种预测性不确定性的度量：熵和MC-DropOut得出的措施。此后，使用不确定性，对另一个CNN进行了培训，以检测当地细分失败，可能需要专家进行校正。最后，模拟了对检测区域的手动校正。使用MICCAI 2017 ACDC挑战中的公开可用的CMR扫描，CNN架构和损耗功能对细分的影响以及不确定性度量。使用手动和自动分割之间的骰子系数和3D Hausdorff距离评估性能。实验表明，将自动分割与模拟的手动校正结合到检测到的分割失败会导致统计学上显着的性能提高。
Segmentation of cardiac anatomical structures in cardiac magnetic resonance images (CMRI) is a prerequisite for automatic diagnosis and prognosis of cardiovascular diseases. To increase robustness and performance of segmentation methods this study combines automatic segmentation and assessment of segmentation uncertainty in CMRI to detect image regions containing local segmentation failures. Three state-of-the-art convolutional neural networks (CNN) were trained to automatically segment cardiac anatomical structures and obtain two measures of predictive uncertainty: entropy and a measure derived by MC-dropout. Thereafter, using the uncertainties another CNN was trained to detect local segmentation failures that potentially need correction by an expert. Finally, manual correction of the detected regions was simulated. Using publicly available CMR scans from the MICCAI 2017 ACDC challenge, the impact of CNN architecture and loss function for segmentation, and the uncertainty measure was investigated. Performance was evaluated using the Dice coefficient and 3D Hausdorff distance between manual and automatic segmentation. The experiments reveal that combining automatic segmentation with simulated manual correction of detected segmentation failures leads to statistically significant performance increase.
