title: An automated bladder volume measurement algorithm by pixel classification using random forests.
abstract: Residual bladder volume measurement is a very important marker for patients with urinary retention problems. To be able to monitor patients with these conditions at the bedside by nurses or in an out patient setting by general physicians, hand held ultrasound devices will be extremely useful. However to increase the usage of these devices by non traditional users, automated tools that can aid them in the scanning and measurement process will be of great help. In our paper, we have developed a robust segmentation algorithm to automatically measure bladder volume by segmenting bladder contours from sagittal and transverse ultrasound views using a combination of machine learning and active contour algorithms. The algorithm is tested on 50 unseen images and 23 transverse and longitudinal image pairs and the performance is reported.
authors.forename: Pavan
authors.initials: P
authors.lastname: Annangi
authors.forename: Sigmund
authors.initials: S
authors.lastname: Frigstad
authors.forename: S B
authors.initials: SB
authors.lastname: Subin
authors.forename: Anders
authors.initials: A
authors.lastname: Torp
authors.forename: Sundararajan
authors.initials: S
authors.lastname: Ramasubramaniam
authors.forename: Srinivas
authors.initials: S
authors.lastname: Varna
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D009929:Organ Size; D010363:Pattern Recognition, Automated; D014463:Ultrasonography / Q000379:methods*; D001743:Urinary Bladder / Q000000981:diagnostic imaging* / Q000473:pathology; D016055:Urinary Retention / Q000000981:diagnostic imaging* / Q000473:pathology