Structure Correction for Robust Volume Segmentation in Presence of Tumors
Structure Correction for Robust Volume Segmentation in Presence of Tumors
复制标题
基于结构校正的肿瘤体积分割算法
DOI：
10.1109/jbhi.2020.3004296
复制
发表时间：
2021-04-01
影响因子：
7.7
通讯作者：
Qin, Hong
中科院分区：
文献类型：
--
作者：
Sahu, Pranjal;Zhao, Yiyuan;Qin, Hong
基于CNN的肺分割模型在缺乏不同训练数据集的情况下，不能在存在大量肿块、疤痕和肿瘤等严重病理情况下分割肺体积。为了解决这一问题，我们提出了一种从CT扫描中分割肺体积的多阶段算法。该算法在第一阶段使用3D CNN来获得左肺和右肺的粗略分割。在第二阶段，使用3D结构校正CNN对分割掩模执行形状校正。采用了一种新的数据增强策略来训练3D CNN，该策略有助于结合全局形状先验。最后，使用并行填充操作对形状校正的分割掩模进行上采样和细化。所提出的多阶段算法在存在大的结节/肿瘤的情况下是稳健的，并且不需要对整个病理肺体积进行标记的分割掩模来进行训练。通过在NSCLC、露娜和LOLA11等公开可用的数据集上进行的广泛实验，我们证明了所提出的方法在不牺牲正常肺部分割精度的情况下，将大的胸膜旁肿瘤体素的召回率提高了至少15%。该方法能在5秒内完成分割，满足了CAD软件的要求，明显快于现有的分割方法。
CNN based lung segmentation models in absence of diverse training dataset fail to segment lung volumes in presence of severe pathologies such as large masses, scars, and tumors. To rectify this problem, we propose a multi-stage algorithm for lung volume segmentation from CT scans. The algorithm uses a 3D CNN in the first stage to obtain a coarse segmentation of the left and right lungs. In the second stage, shape correction is performed on the segmentation mask using a 3D structure correction CNN. A novel data augmentation strategy is adopted to train a 3D CNN which helps in incorporating global shape prior. Finally, the shape corrected segmentation mask is up-sampled and refined using a parallel flood-fill operation. The proposed multi-stage algorithm is robust in the presence of large nodules/tumors and does not require labeled segmentation masks for entire pathological lung volume for training. Through extensive experiments conducted on publicly available datasets such as NSCLC, LUNA, and LOLA11 we demonstrate that the proposed approach improves the recall of large juxtapleural tumor voxels by at least 15% over state-of-the-art models without sacrificing segmentation accuracy in case of normal lungs. The proposed method also meets the requirement of CAD software by performing segmentation within 5 seconds which is significantly faster than present methods.