The expanding role of complex object detection algorithms introduces a need for flexible architectures that simplify interfacing with machine learning techniques and offer easy-to-use training and detection procedures. To address this need, the Integrated Detection Network (IDN) proposes a conceptual design aimed at rapid prototyping of sophisticated systems for object and boundary detection. The IDN uses a strong spatial prior present in the medical imaging domain and a large annotated database of images to train robust detectors. The best detection hypotheses are propagated throughout the detection network using sequential sampling techniques. The effectiveness of the IDN is demonstrated on two learning-based algorithms: (1) automatic detection of fetal brain structures in ultrasound volumes, and (2) liver boundary detection in MRI volumes. Modifying the detection pipeline in both cases is simple and allows for immediate adaptation to the variations of the desired algorithm. Both systems achieved low detection error (3.09 and 4.20 mm for two brain structures and 2.53 mm for boundary).     «

The expanding role of complex object detection algorithms introduces a need for flexible architectures that simplify interfacing with machine learning techniques and offer easy-to-use training and detection procedures. To address this need, the Integrated Detection Network (IDN) proposes a conceptual design aimed at rapid prototyping of sophisticated systems for object and boundary detection. The IDN uses a strong spatial prior present in the medical imaging domain and a large annotated...     »