IEEE Trans Med Imaging. 2023 Oct;42(10):3059-3068. doi: 10.1109/TMI.2023.3273528. Epub 2023 Oct 2.
Liver vessels generated from computed tomography are usually pretty small, which poses major challenges for satisfactory vessel segmentation, including 1) the scarcity of high-quality and large-volume vessel masks, 2) the difficulty in capturing vessel-specific features, and 3) the heavily imbalanced distribution of vessels and liver tissues. To advance, a sophisticated model and an elaborated dataset have been built. The model has a newly conceived Laplacian salience filter that highlights vessel-like regions and suppresses other liver regions to shape the vessel-specific feature learning and to balance vessels against others. It is further coupled with a pyramid deep learning architecture to capture different levels of features, thus improving the feature formulation. Experiments show that this model markedly outperforms the state-of-the-art approaches, achieving a relative improvement of Dice score by at least 1.63% compared to the existing best model on available datasets. More promisingly, the averaged Dice score produced by the existing models on the newly constructed dataset is as high as 0.734±0.070 , which is at least 18.3% higher than that obtained from the existing best dataset under the same settings. These observations suggest that the proposed Laplacian salience, together with the elaborated dataset, can be helpful for liver vessel segmentation.
从计算机断层扫描生成的肝脏血管通常非常小，这给令人满意的血管分割带来了重大挑战，包括 1）高质量和大容量血管掩模的稀缺性，2）捕捉血管特定特征的困难，以及 3）血管和肝脏组织的严重不平衡分布。为了取得进展，构建了一个复杂的模型和一个精心设计的数据集。该模型具有一个新构想的拉普拉斯显著滤波器，突出血管样区域并抑制其他肝脏区域，以形成血管特定特征学习并平衡血管与其他组织。它进一步与金字塔深度学习架构相结合，以捕获不同层次的特征，从而改进特征表述。实验表明，与现有最佳模型相比，该模型明显优于最先进的方法，在可用数据集上，其 Dice 评分的相对提高至少为 1.63%。更有希望的是，在相同设置下，现有模型在新构建的数据集中产生的平均 Dice 评分高达 0.734±0.070，比在现有最佳数据集上获得的评分至少高 18.3%。这些观察结果表明，所提出的拉普拉斯显著滤波器以及精心设计的数据集有助于肝脏血管分割。
