A variational framework for joint detection and segmentation of ovarian cancer metastases

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).