Spatiotemporal Distributions of Acoustic Propagation in Skull During Ultrasound Neuromodulation.

There is widespread interest and concern about the evidence and hypothesis that the auditory system is involved in ultrasound neuromodulation. We have addressed this problem by performing acoustic shear wave simulations in mouse skull and behavioral experiments in deaf mice. The simulation results showed that shear waves propagating along the skull did not reach sufficient acoustic pressure in the auditory cortex to modulate neurons. Behavioral experiments were subsequently performed to awaken anesthetized mice with ultrasound targeting the motor cortex or ventral tegmental area (VTA). The experimental results showed that ultrasound stimulation (US) of the target areas significantly increased arousal scores even in deaf mice, whereas the loss of ultrasound gel abolished the effect. Immunofluorescence staining also showed that ultrasound can modulate neurons in the target area, whereas neurons in the auditory cortex required the involvement of the normal auditory system for activation. In summary, the shear waves propagating along the skull cannot reach the auditory cortex and induce neuronal activation. Ultrasound neuromodulation-induced arousal behavior needs direct action on functionally relevant stimulation targets in the absence of auditory system participation.

Multi-Task Classification and Segmentation for Explicable Capsule Endoscopy Diagnostics.

Capsule endoscopy is a leading diagnostic tool for small bowel lesions which faces certain challenges such as time-consuming interpretation and harsh optical environment inside the small intestine. Specialists unavoidably waste lots of time on searching for a high clearness degree image for accurate diagnostics. However, current clearness degree classification methods are based on either traditional attributes or an unexplainable deep neural network. In this paper, we propose a multi-task framework, called the multi-task classification and segmentation network (MTCSN), to achieve joint learning of clearness degree (CD) and tissue semantic segmentation (TSS) for the first time. In the MTCSN, the CD helps to generate better refined TSS, while TSS provides an explicable semantic map to better classify the CD. In addition, we present a new benchmark, named the Capsule-Endoscopy Crohn's Disease dataset, which introduces the challenges faced in the real world including motion blur, excreta occlusion, reflection, and various complex alimentary scenes that are widely acknowledged in endoscopy examination. Extensive experiments and ablation studies report the significant performance gains of the MTCSN over state-of-the-art methods.

A Hybrid-Attention Nested UNet for Nuclear Segmentation in Histopathological Images.

Nuclear segmentation of histopathological images is a crucial step in computer-aided image analysis. There are complex, diverse, dense, and even overlapping nuclei in these histopathological images, leading to a challenging task of nuclear segmentation. To overcome this challenge, this paper proposes a hybrid-attention nested UNet (Han-Net), which consists of two modules: a hybrid nested U-shaped network (H-part) and a hybrid attention block (A-part). H-part combines a nested multi-depth U-shaped network and a dense network with full resolution to capture more effective features. A-part is used to explore attention information and build correlations between different pixels. With these two modules, Han-Net extracts discriminative features, which effectively segment the boundaries of not only complex and diverse nuclei but also small and dense nuclei. The comparison in a publicly available multi-organ dataset shows that the proposed model achieves the state-of-the-art performance compared to other models.