RESUMO
Vessel contour detection (VCD) in intravascular images is important for the quantitative assessment of vessels. However, it is still a challenging task due to a high degree of morphology variability. Images from a single modality lack sufficient information on the vessel morphology due to the natural limitation of the imaging capability. Therefore, the single-modality VCD methods have difficulty extracting sufficient morphological information. Cross-modality methods have the potential to overcome morphology variability by extracting more information from different modalities. However, they still face the difficulty of the domain discrepancy, i.e., feature space discrepancy and label space inconsistency. In this paper, we aim to address the domain discrepancy for VCD. To overcome label space inconsistency, our method divides the label space into private label space and shared label space. It constructs subdomains for the private label space and the shared label space, and minimizes the task risk at the subdomain level. To overcome feature space discrepancy, it extracts domain-invariant features via domain adaptation between the subdomains. Finally, it uses the domain-invariant features as auxiliary information for each subdomain. Extensive experiments on 130 IVUS sequences (135663 images) and 124 OCT sequences (39857 images) show that our method is effective (e.g., the Dice index [Formula: see text] 0.949), and superior to the nineteen state-of-the-art VCD methods.
RESUMO
Spasticity is a major contributor to pain, disabilities and many secondary complications after stroke. Investigating the effect of spasticity on neuromuscular function in stroke patients may facilitate the development of its clinical treatment, while the underlying mechanism of spasticity still remains unclear. The aim of this study is to explore the difference in the neuromuscular response to passive stretch between healthy subjects and stroke patients with spasticity. Five healthy subjects and three stroke patients with spastic elbow flexor were recruited to complete the passive stretch at four angular velocities (10°/s, 60°/s, 120°/s, and 180°/s) performed by an isokinetic dynamometer. Meanwhile, the 64-channel electromyography (EMG) signals from biceps brachii muscle were recorded. The root mean square (RMS) and fuzzy entropy (FuzzyEn) of EMG recordings of each channel were calculated, and the relationship between the average value of RMS and FuzzyEn over 64-channel was examined. The two groups showed similar performance from results that RMS increased and FuzzyEn decreased with the increment of stretch velocity, and the RMS was negatively correlated with FuzzyEn. The difference is that stroke patients showed higher RMS and lower FuzzyEn during quick stretch than the healthy group. Furthermore, compared with the healthy group, distinct variations of spatial distribution within the spastic muscle were found in the EMG activity of stroke patients. These results suggested that a large number of motor units were recruited synchronously in the presence of spasticity, and this recruitment pattern was non-uniform in the whole muscle. Using a combination of RMS and FuzzyEn calculated from high-density EMG (HD-EMG) recordings can provide an innovative insight into the physiological mechanism underlying spasticity, and FuzzyEn could potentially be used as a new indicator for spasticity, which would be beneficial to clinical intervention and further research on spasticity.
