Name
Title | ||
|---|---|---|
Prostate cancer localization with multispectral MRI using cost-sensitive support vector machines and conditional random fields. |
Abstract | ||
|---|---|---|
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, radiotherapy, 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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TIP.2010.2048612 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
cost-sensitive svm result,accurate prostate cancer localization,prostate cancer,cost-sensitive framework,multispectral mri,biopsy-confirmed cancer patient,automated localization method,conditional random field,cost-sensitive svm,cancer death,human reader,cost-sensitive support vector machine,conditional random fields,surgery,in vivo imaging,cancer,radiation therapy,image segmentation,support vector machines,spatial information,magnetic resonance image,magnetic resonance imaging,ultrasound,support vector machine | Conditional random field,Computer vision,Pattern recognition,Segmentation,Support vector machine,Multispectral image,Image segmentation,Artificial intelligence,Prostate cancer,Cancer,Mathematics,Magnetic resonance imaging | Journal |
Volume | Issue | ISSN |
19 | 9 | 1941-0042 |
Citations | PageRank | References |
36 | 2.03 | 19 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yusuf Artan | 1 | 68 | 10.73 |
| Masoom Haider | 2 | 172 | 22.45 |
| Deanna L Langer | 3 | 82 | 5.05 |
| Theodorus H van der Kwast | 4 | 36 | 2.03 |
| Andrew Evans | 5 | 56 | 4.88 |
| Yongyi Yang | 6 | 1409 | 140.74 |
| Miles N. Wernick | 7 | 595 | 61.13 |
| John Trachtenberg | 8 | 47 | 3.93 |
| Imam Samil Yetik | 9 | 154 | 20.93 |
| van der Kwast, T.H. | 10 | 36 | 2.03 |