A significant amount of research has been done on the segmentation of coronary arteries. However, the resulting automated boundary delineation is still not suitable for clinical utilization. The convolutional neural network was driving advances in the medical image processing. We propose the brief convolutional network (BCN) that automatically produces the labeled mask with the luminal and wall boundaries of the coronary artery. We utilized 50 patients of CCTA - intravascular ultrasound matched image data sets. Training and testing were performed on 40 and 10 patient data sets, respectively. The prediction of luminal and wall mask was performed using stacked BCN on the each image view: axial, coronal, and sagittal of straightened curved planar reformation. We defined the vector that includes probability from BCN result on each image view and proposed amplified probability. We used an Adaptive Boost regressor with an extremely randomized tree regressor to determine the label for unknown probability vector.
|Title of host publication||2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017|
|Publisher||IEEE Computer Society|
|Number of pages||4|
|Publication status||Published - 2017 Jun 15|
|Event||14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 - Melbourne, Australia|
Duration: 2017 Apr 18 → 2017 Apr 21
|Name||Proceedings - International Symposium on Biomedical Imaging|
|Other||14th IEEE International Symposium on Biomedical Imaging, ISBI 2017|
|Period||17/4/18 → 17/4/21|
Bibliographical notePublisher Copyright:
© 2017 IEEE.
All Science Journal Classification (ASJC) codes
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging