Prostate Cancer Localization With Multispectral MRI Using Cost-Sensitive Support Vector Machines and Conditional Random Fields
Prostate Cancer Localization With Multispectral MRI Using Cost-Sensitive Support Vector Machines and Conditional Random Fields
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DOI：
10.1109/tip.2010.2048612
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发表时间：
2010-09-01
影响因子：
10.6
通讯作者：
Yetik, Imam Samil
中科院分区：
文献类型：
--
作者：
Artan, Yusuf;Haider, Masoom A.;Yetik, Imam Samil
前列腺癌是美国男性癌症死亡的主要原因。幸运的是，早期诊断患者的生存率相对较高。因此，体内成像对于疾病的检测和治疗起着重要作用。非侵入性成像的精确前列腺癌定位可用于指导活检、放射治疗和手术以及监测疾病进展。与经直肠超声（TRUS）相比，使用直肠内线圈进行的磁共振成像（MRI）可提供更高的前列腺癌定位准确性。然而，一般来说，单一类型的MRI不足以进行可靠的肿瘤定位。作为替代方案，多谱MRI，即，多个MRI衍生数据集的使用已经成为用于前列腺癌定位的有前景的非侵入性成像技术;然而，几乎所有的研究都是使用人类读者。对于人类读者来说，存在显著的观察者间和观察者内差异，并且人类很难分析多光谱MRI的大数据集。为了解决这些问题，本研究提出了一种自动定位方法，使用成本敏感的支持向量机（SVMs），并表明，这种方法的结果在提高定位精度比经典的SVM。此外，我们开发了一种新的分割方法，结合条件随机场（CRF）与成本敏感的框架，并表明我们的方法进一步提高了成本敏感的SVM结果，结合空间信息。我们测试支持向量机，成本敏感的支持向量机，并提出了成本敏感的CRF多光谱MRI数据集从21活检证实的癌症患者。我们的研究结果表明，多光谱MRI有助于提高前列腺癌定位的准确性相比，单一的MR图像，并使用先进的方法，如成本敏感的SVM以及提出的成本敏感的CRF可以提高性能显着时，SVM。
Prostate cancer is a leading cause of cancer death for men in the United States. Fortunately, the survival rate for early diagnosed patients is relatively high. Therefore, in vivo imaging plays an important role for the detection and treatment of the disease. Accurate prostate cancer localization with noninvasive imaging can be used to guide biopsy, radiotheraphy, and surgery as well as to monitor disease progression. Magnetic resonance imaging (MRI) performed with an endorectal coil provides higher prostate cancer localization accuracy, when compared to transrectal ultrasound (TRUS). However, in general, a single type of MRI is not sufficient for reliable tumor localization. As an alternative, multispectral MRI, i.e., the use of multiple MRI-derived datasets, has emerged as a promising noninvasive imaging technique for the localization of prostate cancer; however almost all studies are with human readers. There is a significant inter and intraobserver variability for human readers, and it is substantially difficult for humans to analyze the large dataset of multispectral MRI. To solve these problems, this study presents an automated localization method using cost-sensitive support vector machines (SVMs) and shows that this method results in improved localization accuracy than classical SVM. Additionally, we develop a new segmentation method by combining conditional random fields (CRF) with a cost-sensitive framework and show that our method further improves cost-sensitive SVM results by incorporating spatial information. We test SVM, cost-sensitive SVM, and the proposed cost-sensitive CRF on multispectral MRI datasets acquired from 21 biopsy-confirmed cancer patients. Our results show that multispectral MRI helps to increase the accuracy of prostate cancer localization when compared to single MR images; and that using advanced methods such as cost-sensitive SVM as well as the proposed cost-sensitive CRF can boost the performance significantly when compared to SVM.