Name
Abstract | ||
|---|---|---|
Automatically segmenting organs in monocular laparoscopic images is an important and challenging research objective in computer-assisted intervention. For the uterus this is difficult because of high inter-patient variability in tissue appearance and low-contrast boundaries with the surrounding peritoneum. We present a framework to segment the uterus which is completely automatic, requires only a single monocular image, and does not require a 3D model. Our idea is to use a patient-independent uterus detector to roughly localize the organ, which is then used as a supervisor to train a patient-specific organ segmenter. The segmenter uses a physically-motivated organ boundary model designed specifically for illumination in laparoscopy, which is fast to compute and gives strong segmentation constraints. Our segmenter uses a lightweight CRF that is solved quickly and globally with a single graphcut. On a dataset of 220 images our method obtains a mean DICE score of 92.9%. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-24574-4_22 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Conditional random field,Supervisor,Computer vision,Uterus,Market segmentation,Pattern recognition,Segmentation,Computer science,Monocular image,Artificial intelligence,Monocular,Detector | Conference | 9351 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Toby Collins | 1 | 57 | 7.10 |
| Adrien Bartoli | 2 | 1147 | 89.14 |
| Nicolas Bourdel | 3 | 14 | 3.22 |
| Michel Canis | 4 | 26 | 3.91 |