Pancreas Segmentation in CT and MRI via Task-Specific Network Design and Recurrent Neural Contextual Learning
Pancreas Segmentation in CT and MRI via Task-Specific Network Design and Recurrent Neural Contextual Learning
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DOI：
10.1007/978-3-030-13969-8_1
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发表时间：
2019-01-01
期刊：
影响因子：
--
通讯作者：
Yang, Lin
中科院分区：
文献类型：
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作者：
Cai, Jinzheng;Lu, Le;Yang, Lin
放射学图像中的自动胰腺分割，例如，计算机辅助筛选、诊断和定量评估经常需要计算机断层扫描（CT）和磁共振成像（MRI）。然而，胰腺是一个具有挑战性的腹部器官分割，由于高患者间的解剖变异性的形状和体积度量。最近，卷积神经网络（CNN）在胰腺的准确分割方面表现出了良好的性能。然而，基于CNN的方法往往遭受分割不连续的原因，如噪声图像质量和模糊的胰腺边界。在本章中，我们首先讨论CNN配置和训练目标，这些配置和训练目标导致胰腺分割的最新性能。然后，我们提出了一个递归神经网络（RNN）来解决相邻图像切片之间的分割空间不一致的问题。RNN获取CNN的输出，并通过提高形状平滑度来细化分割。
Automatic pancreas segmentation in radiology images, e.g., computed tomography (CT), and magnetic resonance imaging (MRI), is frequently required by computer-aided screening, diagnosis, and quantitative assessment. Yet, pancreas is a challenging abdominal organ to segment due to the high inter-patient anatomical variability in both shape and volume metrics. Recently, convolutional neural networks (CNN) have demonstrated promising performance on accurate segmentation of pancreas. However, the CNN-based method often suffers from segmentation discontinuity for reasons such as noisy image quality and blurry pancreatic boundary. In this chapter, we first discuss the CNN configurations and training objectives that lead to the state-of-the-art performance on pancreas segmentation. We then present a recurrent neural network (RNN) to address the problem of segmentation spatial inconsistency across adjacent image slices. The RNN takes outputs of the CNN and refines the segmentation by improving the shape smoothness.