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Proceedings Paper

Multi-scale feature learning on pixels and super-pixels for seminal vesicles MRI segmentation
Author(s): Qinquan Gao; Akshay Asthana; Tong Tong; Daniel Rueckert; Philip "Eddie" Edwards
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Paper Abstract

We propose a learning-based approach to segment the seminal vesicles (SV) via random forest classifiers. The proposed discriminative approach relies on the decision forest using high-dimensional multi-scale context-aware spatial, textual and descriptor-based features at both pixel and super-pixel level. After affine transformation to a template space, the relevant high-dimensional multi-scale features are extracted and random forest classifiers are learned based on the masked region of the seminal vesicles from the most similar atlases. Using these classifiers, an intermediate probabilistic segmentation is obtained for the test images. Then, a graph-cut based refinement is applied to this intermediate probabilistic representation of each voxel to get the final segmentation. We apply this approach to segment the seminal vesicles from 30 MRI T2 training images of the prostate, which presents a particularly challenging segmentation task. The results show that the multi-scale approach and the augmentation of the pixel based features with the super-pixel based features enhances the discriminative power of the learnt classifier which leads to a better quality segmentation in some very difficult cases. The results are compared to the radiologist labeled ground truth using leave-one-out cross-validation. Overall, the Dice metric of 0:7249 and Hausdorff surface distance of 7:0803 mm are achieved for this difficult task.

Paper Details

Date Published: 21 March 2014
PDF: 6 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 903407 (21 March 2014); doi: 10.1117/12.2043893
Show Author Affiliations
Qinquan Gao, Imperial College London (United Kingdom)
Akshay Asthana, Imperial College London (United Kingdom)
Tong Tong, Imperial College London (United Kingdom)
Daniel Rueckert, Imperial College London (United Kingdom)
Philip "Eddie" Edwards, Imperial College London (United Kingdom)


Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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