A top-down manner-based DCNN architecture for semantic image segmentation

<div><p>Given their powerful feature representation for recognition, deep convolutional neural networks (DCNNs) have been driving rapid advances in high-level computer vision tasks. However, their performance in semantic image segmentation is still not satisfactory. Based on the analysis of visual mechanism, we conclude that DCNNs in a bottom-up manner are not enough, because semantic image segmentation task requires not only recognition but also visual attention capability. In the study, superpixels containing visual attention information are introduced in a top-down manner, and an extensible architecture is proposed to improve the segmentation results of current DCNN-based methods. We employ the current state-of-the-art fully convolutional network (FCN) and FCN with conditional random field (DeepLab-CRF) as baselines to validate our architecture. Experimental results of the PASCAL VOC segmentation task qualitatively show that coarse edges and error segmentation results are well improved. We also quantitatively obtain about 2%-3% intersection over union (IOU) accuracy improvement on the PASCAL VOC 2011 and 2012 test sets.</p></div

Evaluation results of the PASCAL VOC 2012 test set.

<p>Evaluation results of the PASCAL VOC 2012 test set.</p

Superpixel segmentation results from the GS method.

<p><b>Different colors represent different superpixels.</b> (a) Input image (b) Superpixels from GS method (c) Semantic labels (d) Superpixels from GS method with semantic labels.</p

Examples that our method based on DBSCAN superpixels produced better results than the FCN model.

<p><b>Different colors represent different classes.</b> (a) Input image (b) Segmentation results from FCN (c) Segmentation results from FCN-DBSCAN (d) FCN-DBSCAN-v2 (e) Ground truth.</p

Performance of our proposed models on the PASCAL VOC 2011 and 2012 test sets compared to other state-of-art methods.

<p>Performance of our proposed models on the PASCAL VOC 2011 and 2012 test sets compared to other state-of-art methods.</p

Genotype-Phenotype Correlation in Chinese Patients with Spinal and Bulbar Muscular Atrophy

<div><p>Spinal and bulbar muscular atrophy (SBMA) is an X-linked recessive motor neuron disease characterized by slowly progressive weakness and atrophy of proximal limbs and bulbar muscles. To assess the genotype-phenotype correlation in Chinese patients, we identified 155 patients with SBMA and retrospectively examined available data from laboratory tests and neurophysiological analyses. Correlations between genotype and phenotype were analyzed. There was an inverse correlation between the length of CAG repeats and age at first muscle weakness (<i>p<0</i>.<i>0001</i>). The serum creatine kinase level showed a significant inverse correlation with disease duration and the age at examination (<i>p=0</i>.<i>019</i> and <i>p=0</i>.<i>004</i>, respectively). Unlike previous classification of motor- and sensory-dominant phenotypes, all findings of nerve conduction, except the amplitudes of median nerve compound motor action potential, were positively correlated to the length of CAG repeats. A significant decline in sensory nerve action potential amplitudes may assist differential diagnosis of SBMA.</p></div

Coarse semantic segmentation results of the PASCAL VOC dataset based on the FCN and DeepLab-CRF model.

<p><b>Different colors represent different classes.</b> (a) Input image (b) Segmentation results from FCN (first two rows) and DeepLab-CRF (last two rows) (c) Ground truth.</p

Examples that our method based on GS superpixels produced better results than the FCN model.

<p><b>Different colors represent different classes.</b> (a) Input image (b) Segmentation results from FCN (c) Segmentation results from FCN-GS (d) FCN-GS-v2 (e) Ground truth.</p

Demographics of spinal and bulbar muscular atrophy (SBMA) patients.

<p>Demographics of spinal and bulbar muscular atrophy (SBMA) patients.</p

Relationship between the number of CAG repeats and the age at first muscle weakness.

<p>There is an inverse relationship between the number of CAG repeats and the age at first muscle weakness (R² = 0.34, <i>p<0</i>.<i>0001</i>).</p