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  • Title: [Analysis of the performance of a multi-view fusion and active contour constraint based deep learning algorithm for ossicles segmentation on 10 μm otology CT].
    Author: Zhu ZY, Li XG, Wang RX, Tang RW, Zhao L, Yin GX, Wang ZC, Zhuo L.
    Journal: Zhonghua Yi Xue Za Zhi; 2021 Dec 21; 101(47):3897-3903. PubMed ID: 34905891.
    Abstract:
    Objective: To explore the performance of a deep learning algorithm that combined multi-view fusion with active contour constrained for ossicles segmentation on the 10 μm otology CT images. Methods: The 10 μm otology CT image data from 79 cases (56 cases were from volunteers and 23 cases were from specimens) were retrospectively collected in the Radiology Department of Beijing Friendship Hospital from October 2019 to December 2020. An annotation of malleus, incus, and stapes were conducted. Then the datasets were established and were divided into training set (n=55), validation set (n=8), and test set (n=16). Using the rapid localization of the region of interest combined with the precise segmentation algorithm, the malleus, incus and stapes were segmented and fused from three perspectives of coronal, sagittal and cross-sectional views. Besides, an active contour loss was designed simultaneously for the segmentation of stapes. Dice similarity coefficient (DSC) was used as the objective evaluation metric for the evaluation of the segmentation results. The inter group DSC of the proposed method was compared with that of the basic method and other methods. Results: The average DSC values of the multi-view fusion segmentation algorithm for malleus, incus and stapes reached up to 94.2%±2.7%, 94.6%±2.6% and 76.0%±5.5%, respectively. After adopting the constraint of active contour loss method, the average DSC of stapes was improved (76.4%±5.4% vs 76.0%±5.5%). The visualization results also demonstrated that the segmentation results of the stapes were more complete. Conclusions: Multi-view fusion algorithm based on 10 μm otology CT images can realize accurate segmentation of malleus and incus. Combined with the constraint of active contour loss method, the segmentation accuracy of stapes can be further improved. 目的： 探讨多视角融合以及主动轮廓约束的深度学习算法在10 μm级耳科专用CT图像上对听小骨分割的效果。 方法： 回顾性收集2019年10月至2020年12月北京友谊医院放射科10 μm级耳科专用CT检查的受试者数据共79侧耳（56侧来自志愿者，23侧来自标本）。对锤骨、砧骨和镫骨进行标注，将其划分为训练集（55侧）、验证集（8侧）和测试集（16侧）。采用感兴趣区域快速定位结合精准分割算法，分别从冠状面、矢状面和横断面3个视角对锤骨、砧骨和镫骨进行分割与融合。针对镫骨，同时设计了基于主动轮廓损失的镫骨分割方法。分割实验采用客观指标Dice相似系数（DSC）作为判别标准，比较本方法与基础方法、本方法与其他分割方法的组间DSC差异。 结果： 多视角融合分割算法对锤骨、砧骨和镫骨的平均DSC值分别为94.2%±2.7%、94.6%±2.6%和76.0%±5.5%；结合主动轮廓损失的约束方法后，对镫骨的平均DSC值进一步提升（76.4%±5.4%比76.0%±5.5%），且可视化结果显示镫骨结构的分割结果更加完整。 结论： 基于10 μm级耳科专用CT数据的多视角融合算法可实现对锤骨和砧骨结构的精准分割，结合主动轮廓损失约束方法，可进一步提升对镫骨结构的分割精度。.
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