Detection of Fetal Anatomies from Ultrasound Images using a Constrained Probabilistic Boosting Tree

We propose a novel method for the automatic detection and measurement of fetal anatomical structures i n ultrasound images. This problem offers a myriad of challenges, including: difficulty of modeling the appearance variations of the visual object of interest; robustness to speckle noise and s ignal drop-out; and large search space of the detection procedure . Previous solutions typically rely on the explicit encoding of prior knowledge and formulation of the problem as a perceptual grouping task solved through clustering or variational approaches. These methods are constrained by the validity o f the underlying assumptions and usually are not enough to captur e the complex appearances of fetal anatomies. We propose a novel system for fast automatic detection and measurement o f fetal anatomies that directly exploits a large database of e xpert annotated fetal anatomical structures in ultrasound images. Our method learns automatically to distinguish between the appearance of the object of interest and background by training a constrained probabilistic boosting tree classifier. Thissystem is able to produce the automatic segmentation of several fet al anatomies using the same basic detection algorithm. We show results on fully automatic measurement of biparietal diameter (BPD), head circumference (HC), abdominal circumference ( AC), femur length (FL), humerus length (HL), and crown rump length (CRL). Notice that our approach is the first in the literature to deal with the HL and CRL measurements. Extensive experiment s (with clinical validation) show that our system is, on average, close to the accuracy of experts in terms of segmentation and obstetric measurements. Finally this system runs under hal f second on a standard dual-core PC computer.