Name
Abstract | ||
|---|---|---|
Ovarian quantification by volume measurement is performed in routine clinical practice for diagnosis and management of gynecological conditions such as infertility. This paper describes an automated algorithm for ovarian volume measurement using three-dimensional transvaginal ultrasound (TVUS) images. The algorithm integrates deep learned energy map as a soft shape prior within the variational segmentation framework for 2D radial slice segmentation. The output of the segmentation framework is used for mesh generation using U-V spherical parametrization to estimate the surface of the ovarian volume. The segmentation framework provides approximately 86% average Dice overlap with the ground truth annotations. The mean absolute volume difference is found to be approximately 5ml between manual and automated measurements on 55 TVUS images. |
Year | Venue | Keywords |
|---|---|---|
2018 | 2018 IEEE 15TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2018) | Ovary, Ultrasound, Segmentation, U-V parametrization, Deep-Learning |
Field | DocType | ISSN |
Computer vision,Pattern recognition,Segmentation,Computer science,Volume measurement,Clinical Practice,Ground truth,Artificial intelligence,Mesh generation,Absolute volume,Ultrasound | Conference | 1945-7928 |
Citations | PageRank | References |
1 | 0.40 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ravi Teja Narra | 1 | 1 | 0.40 |
| Nitin Singhal | 2 | 113 | 10.55 |
| Nikhil S Narayan | 3 | 3 | 1.16 |
| G. A. Ramaraju | 4 | 1 | 0.73 |