基于形态学Snakes和深度卷积神经网络的胰腺检测与分割
Detection and Segmentation of Pancreas using Morphological Snakes and Deep Convolutional Neural Networks
摘要：
胰腺癌是最致命的癌症类型之一，25%的确诊患者只能存活一年，6%的患者存活五年。计算机断层扫描（CT）筛查试验在改善胰腺癌的早期发现方面发挥了关键作用，这表明患者生存率显着提高。然而，这种图像的高级分析通常需要手动分割胰腺，这是一项耗时的任务。此外，胰腺在形状上呈现出高度可变性，同时仅占据整个腹部CT扫描的非常小的区域，这增加了问题的复杂性。深度学习的快速发展有助于提供稳健的算法，这些算法提供廉价、准确和用户独立的分割结果，可以指导领域专家。本文通过研究胰腺分割的两步方法来解决这一任务，通过先前的粗略定位或检测胰腺来辅助任务。胰腺的粗略定位由估计的概率图提供，检测任务通过使用YOLOv 4深度学习算法来实现。分割任务是解决了一个修改后的U-网模型应用于裁剪数据，以及通过使用形态学活动轮廓算法。为了进行比较，还将U-Net模型应用于完整的CT图像，其提供粗略的胰腺分割以用作参考。检测网络在美国国立卫生研究院（NIH）数据集和医学分割十项全能中的胰腺肿瘤任务数据集上的实验结果显示平均精度为50.67%。最佳分割网络在NIH数据集上取得了良好的分割效果，达到了67.67%的Dice分数。
英文摘要：
Pancreatic cancer is one of the deadliest types of cancer, with 25% of the diagnosed patients surviving for only one year and 6% of them for five. Computed tomography (CT) screening trials have played a key role in improving early detection of pancreatic cancer, which has shown significant improvement in patient survival rates. However, advanced analysis of such images often requires manual segmentation of the pancreas, which is a time-consuming task. Moreover, pancreas presents high variability in shape, while occupying only a very small area of the entire abdominal CT scans, which increases the complexity of the problem. The rapid development of deep learning can contribute to offering robust algorithms that provide inexpensive, accurate, and user-independent segmentation results that can guide the domain experts. This dissertation addresses this task by investigating a two-step approach for pancreas segmentation, by assisting the task with a prior rough localization or detection of pancreas. This rough localization of the pancreas is provided by an estimated probability map and the detection task is achieved by using the YOLOv4 deep learning algorithm. The segmentation task is tackled by a modified U-Net model applied on cropped data, as well as by using a morphological active contours algorithm. For comparison, the U-Net model was also applied on the full CT images, which provide a coarse pancreas segmentation to serve as reference. Experimental results of the detection network on the National Institutes of Health (NIH) dataset and the pancreas tumour task dataset within the Medical Segmentation Decathlon show 50.67% mean Average Precision. The best segmentation network achieved good segmentation results on the NIH dataset, reaching 67.67% Dice score.
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