Multi-organ localization with cascaded global-to-local regression and shape prior
Multi-organ localization with cascaded global-to-local regression and shape prior
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DOI：
10.1016/j.media.2015.04.007
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发表时间：
2015-07-01
影响因子：
10.9
通讯作者：
Bloch, Isabelle
中科院分区：
文献类型：
--
作者：
Gauriau, Romane;Cuingnet, Remi;Bloch, Isabelle
提出了一种快速、准确、鲁棒的医学图像器官定位方法。我们将回归随机森林的全局到局部级联推广到多个器官。第一回归器编码器官之间的全局关系，同时学习所有器官参数。然后，随后的回归器在局部和独立地细化每个器官的定位，以提高准确性。通过结合回归投票分布和器官形状先验（通过概率图谱表示），我们计算出器官专用概率图的置信度图。它们在级联本身中使用，以便更好地为第二组回归器选择测试体素，并由于形状先验而提供比经典边界框结果更丰富的信息。我们建议对不同的学习和测试参数进行广泛的研究，显示它们对合理扰动的鲁棒性及其对最终算法精度的影响。最后，我们通过评估六个腹部器官（肝脏、两个肾脏、脾脏、胆囊和胃）在130个CT体积的大型多样化数据库中的定位，证明了我们方法的稳健性和准确性。此外，将我们的结果与现有的两种方法进行了比较，表明我们的方法带来了显著的改进，以及我们对参数的深入理解和优化。(C) 2015 Elsevier B.V.版权所有
We propose a method for fast, accurate and robust localization of several organs in medical images. We generalize the global-to-local cascade of regression random forest to multiple organs. A first regressor encodes the global relationships between organs, learning simultaneously all organs parameters. Then subsequent regressors refine the localization of each organ locally and independently for improved accuracy. By combining the regression vote distribution and the organ shape prior (through probabilistic atlas representation) we compute confidence maps that are organ-dedicated probability maps. They are used within the cascade itself, to better select the test voxels for the second set of regressors, and to provide richer information than the classical bounding boxes result thanks to the shape prior.We propose an extensive study of the different learning and testing parameters, showing both their robustness to reasonable perturbations and their influence on the final algorithm accuracy. Finally we demonstrate the robustness and accuracy of our approach by evaluating the localization of six abdominal organs (liver, two kidneys, spleen, gallbladder and stomach) on a large and diverse database of 130 CT volumes. Moreover, the comparison of our results with two existing methods shows significant improvements brought by our approach and our deep understanding and optimization of the parameters. (C) 2015 Elsevier B.V. All rights reserved.