Minjeong Kim, Guorong Wu, Dinggang Shen (auth.), Guorong Wu,'s Machine Learning in Medical Imaging: 4th International PDF

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By Minjeong Kim, Guorong Wu, Dinggang Shen (auth.), Guorong Wu, Daoqiang Zhang, Dinggang Shen, Pingkun Yan, Kenji Suzuki, Fei Wang (eds.)

This booklet constitutes the refereed complaints of the 4th overseas Workshop on computer studying in scientific Imaging, MLMI 2013, held along with the overseas convention on scientific picture Computing and computing device Assisted Intervention, MICCAI 2013, in Nagoya, Japan, in September 2013. The 32 contributions incorporated during this quantity have been rigorously reviewed and chosen from fifty seven submissions. They concentrate on significant traits and demanding situations within the region of computing device studying in clinical imaging and goal to spot new state-of-the-art innovations and their use in scientific imaging.

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Additional resources for Machine Learning in Medical Imaging: 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013. Proceedings

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The computation time for both the original multi-atlas ABAS method and the RFenhanced one is quite comparable. 66 GHz CPU and a Learning-Boosted Label Fusion for Multi-atlas Auto-Segmentation 23 NVIDIA GTX 480 graphics card. The STAPLE label fusion takes less than a minute. Computing the RF classification only adds one extra minute, which is about 1/10-th of the total computation time assuming 9 atlases are used. 2 Ribcage Segmentation In the second experiment, we test the proposed method on ribcage segmentation of lung CT images.

Each image measurement plane will result in a different apparent width of the lateral ventricle leading to measurement error. This problem is a general one, and equally a problem for measurement of other key fetal brain structures such as the head G. Wu et al. ): MLMI 2013, LNCS 8184, pp. 25–32, 2013. © Springer International Publishing Switzerland 2013 26 M. Yaqub et al. circumference, biparietal diameter (BPD), cisterna magna, and anterior and posterior horns of the lateral ventricles. In contrast, volumetric measurements on 3D fetal brain images could overcome this problem by estimating the complete volume of structures.

Note that the ultrasound images are normally captured in transverse view when constructing a three-dimensional structure, unlike when measuring intima-media thickness, where the ultrasound images are captured in longitudinal view. The inside and outside boundaries of the carotid wall are then extracted from the ultrasound images by applying a contour extraction technique, such as an active contour model. Finally, the extracted contours are connected to build the three-dimensional carotid wall structure.

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