LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images

Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and ongoing maturation and myelination processes. In the first year of life, the image contrast between white and gray matters of the infant brain undergoes dramatic changes. In particular, the image contrast is inverted around 6-8 months of age, and the white and gray matter tissues are isointense in both T1- and T2-weighted MR images and thus exhibit the extremely low tissue contrast, which poses significant challenges for automated segmentation. Most previous studies used multi-atlas label fusion strategy, which has the limitation of equally treating the different available image modalities and is often computationally expensive. To cope with these limitations, in this paper, we propose a novel learning-based multi-source integration framework for segmentation of infant brain images. Specifically, we employ the random forest technique to effectively integrate features from multi-source images together for tissue segmentation. Here, the multi-source images include initially only the multi-modality (T1, T2 and FA) images and later also the iteratively estimated and refined tissue probability maps of gray matter, white matter, and cerebrospinal fluid. Experimental results on 119 infants show that the proposed method achieves better performance than other state-of-the-art automated segmentation methods. Further validation was performed on the MICCAI grand challenge and the proposed method was ranked top among all competing methods. Moreover, to alleviate the possible anatomical errors, our method can also be combined with an anatomically-constrained multi-atlas labeling approach for further improving the segmentation accuracy

Differentiating patients with radiculopathy from chronic low back pain patients by single surface EMG parameter

The classification potential of surface electromyographic (EMG) parameters needs to be explored beyond classification of subjects onto low back pain subjects and control subjects. In this paper, a classification model based on surface EMG parameter is introduced to differentiate low back pain patients with radiculopathy from chronic low back pain (CLBP) patients and control subjects. A variant of the Roman chair was used to perform static contractions, where subject's own upper body weight was used to induce muscle fatigue in low back muscles. Surface EMG signals were recorded over the paraspinal muscles at L1–L2 and L4–L5 interspace level. As a descriptor of spectral changes, the median frequency of the power spectrum (MDF) was estimated by use of Hilbert–Huang transform. Student's t-test detected that regression line slope of the median frequency is significantly different (p < 0.05) only between low back pain patients with radiculopathy and other two groups. There was no significant difference between CLBP patients and control subjects. The achieved overall accuracy of the implemented decision tree classification model was at best 86.8%. The results suggest possibility of differentiating low back pain patients to subgroups depending on clinical symptoms