Tumor sensitive matching flow: A variational method to detecting and segmenting perihepatic and perisplenic ovarian cancer metastases on contrast-enhanced abdominal CT.
Accurate automated segmentation and detection of ovarian cancer metastases may improve the diagnosis and prognosis of women with ovarian cancer. In this paper, we focus on an important subset of ovarian cancer metastases that spread to the surface of the liver and spleen. Automated ovarian cancer metastasis detection and segmentation are very challenging problems to solve. These metastases have a wide variety of shapes and intensity values similar to that of the liver, spleen and adjacent soft tissues. To address these challenges, this paper presents a variational approach, called tumor sensitive matching flow (TSMF), to detect and segment perihepatic and perisplenic ovarian cancer metastases. TSMF is an image motion field that only highlights metastasis-caused deformation on the surface of liver and spleen while dampening all other image motion between the patient image and the atlas image. It provides several benefits: (1) juxtaposing the roles of image matching and metastasis classification within a variational framework; (2) only requiring a small set of features from a few patient images to train a metastasis-likelihood function for classification; and (3) dynamically creating shape priors for geodesic active contour (GAC) to prevent inaccurate metastasis segmentation. We compared the TSMF to an organ surface partition (OSP) baseline approach. At a false positive rate of 2 per patient, the sensitivities of TSMF and OSP were 87% and 17% (p<0.001), respectively. In a comparison of the segmentations conducted using TSMF-constrained GAC and conventional GAC, the volume overlap rates were 73 ± 9% and 46 ± 26% (p<0.001) and average surface distances were 2.4 ± 1.2 mm and 7.0 ± 6.0 mm (p<0.001), respectively. These encouraging results demonstrate that TSMF could accurately detect and segment ovarian cancer metastases.
Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.
Jianfei
J
Liu
Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.
Shijun
S
Wang
Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Medical Center, Washington, DC, USA; Departments of Radiology and Pediatrics, School of Medicine and Health Sciences, George Washington University, Washington, DC, USA.
Marius George
MG
Linguraru
Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.
Jianhua
J
Yao
Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA. Electronic address: rms@nih.gov.
Ronald M
RM
Summers
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000704:Analysis of Variance; D003287:Contrast Media; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008113:Liver Neoplasms / Q000000981:diagnostic imaging* / Q000556:secondary*; D008875:Middle Aged; D010051:Ovarian Neoplasms / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D011860:Radiography, Abdominal / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013160:Splenic Neoplasms / Q000000981:diagnostic imaging* / Q000556:secondary*; D014057:Tomography, X-Ray Computed / Q000379:methods*; D047368:Tumor Burden; D055815:Young Adult
D003287:Contrast Media
Dynamic shape prior; Level set; Ovarian cancer Metastases; Tumor sensitive matching flow