Title :
Robust shape-constrained active contour for whole heart segmentation in 3-D CT images for radiotherapy planning
Author :
Xuan Zhao ; Yao Wang ; Jozsef, Gabor
Author_Institution :
Polytech. Sch. of Eng., Electr. & Comput. Eng., NYU, New York, NY, USA
Abstract :
Automatic segmentation of the whole heart in computed tomography(CT) image is crucial for efficient treatment planning of thoracic radiotherapy. In this paper, we propose a fully automatic method for whole heart segmentation of thoracic CT images. A robust active shape model (Robust ASM) is proposed using shape models developed from training data to reduce outliers due to similar intensity of neighboring organs. A novel shape constrained active contour model is presented to further improve the segmentation result. A mean point-to-surface error of 2.37mm was measured based on 38 images. The averaged Dice index is 0.90.
Keywords :
cardiology; computerised tomography; edge detection; geometry; image segmentation; learning (artificial intelligence); medical image processing; physiological models; planning; radiation therapy; statistical analysis; 3D thoracic CT image; averaged Dice index; computed tomography image; fully automatic whole heart segmentation; mean point-to-surface error; outlier reduction; robust ASM; robust active shape model; robust shape-constrained active contour model; similar neighboring organ intensity; thoracic radiotherapy planning; training data; Active contours; Computed tomography; Heart; Image segmentation; Robustness; Shape; Training; CT image; Radiotherapy; Whole Heart Segmentation;
Conference_Titel :
Image Processing (ICIP), 2014 IEEE International Conference on
Conference_Location :
Paris
DOI :
10.1109/ICIP.2014.7024999