Paper Info
Title
A variational framework for joint detection and segmentation of ovarian cancer metastases.
Abstract
Detection and segmentation of ovarian cancer metastases have great clinical impacts on women's health. However, the random distribution and weak boundaries of metastases significantly complicate this task. This paper presents a variational framework that combines region competition based level set propagation and image matching flow computation to jointly detect and segment metastases. Image matching flow not only detects metastases, but also creates shape priors to reduce over-segmentation. Accordingly, accurate segmentation helps to improve the detection accuracy by separating flow computation in metastasis and non-metastasis regions. Since all components in the image processing pipeline benefit from each other, our joint framework can achieve accurate metastasis detection and segmentation. Validation on 50 patient datasets demonstrated that our joint approach was superior to a sequential method with sensitivity 89.2% vs. 81.4% (Fisher exact test p = 0.046) and false positive per patient 1.04 vs. 2.04. The Dice coefficient of metastasis segmentation was 92 +/- 5.2% vs. 72 +/- 8% (paired t-test p = 0.022), and the average surface distance was 1.9 +/- 1.5mm vs. 4.5 +/- 2.2mm (paired t-test p = 0.004).
Year
DOI
Venue
2013
10.1007/978-3-642-40763-5_11
Lecture Notes in Computer Science
Keywords
Field
DocType
Ovarian Cancer Metastasis,Joint Detection and Segmentation,Level Set,Dynamic Shape Prior
Metastasis,Fisher's exact test,Computer vision,Pattern recognition,Segmentation,Computer science,Sørensen–Dice coefficient,Image processing,Level set,Artificial intelligence,Prior probability,Computation
Conference
Volume
Issue
ISSN
8150
Pt 2
0302-9743
Citations 
PageRank 
References 
1
0.34
7
Authors
5
Name
Order
Citations
PageRank
Jianfei Liu18112.98
Shijun Wang223922.83
Marius George Linguraru336248.94
Jianhua Yao41135110.49
Ronald M. Summers589386.16