脉络膜丛（CP）是产生大部分脑脊液（CSF）的大脑的心室的结构。几个淘汰的课后和体内研究已经指出了它们在多发性硬化症（MS）中的炎症过程中的作用。因此，来自MRI的CP的自动分割具有高价值，用于研究其在大型患者的大队列中的特征。据我们所知，CP分段唯一可自由的工具是FreeSurfer，但其对该特定结构的准确性很差。在本文中，我们建议自动从非对比度增强的T1加权MRI自动分段。为此，我们介绍了一种基于轴向多层截图（MLP）的组件的“Axial-MLP”的新模型。这是最近的作品启发，表明，变压器的自我注意层可以用MLPS取代。系统地与标准的3D U-Net，NNU-Net，FreeSurfer和Fastsurefer系统地进行系统地进行系统地进行系统地进行。对于我们的实验，我们利用141个受试者的数据集（44个对照和97名MS患者）。我们展示所有测试的深度学习（DL）方法优于FreeSurfer（DIC为0.7的骰子，对于FreeSurfer的DL 0.33）。 Axial-MLP与U-Net竞争竞争，即使它略有略低于准确。我们纸张的结论是两倍：1）学习的深度学习方法可能是研究CP在MS患者的大型队列中的有用工具; 2）〜Axial-MLP是用于这种任务的卷积神经网络的潜在可行的替代方案，尽管它可以从进一步的改进中受益。

Diffusion models have shown great promise for image generation, beating GANs in terms of generation diversity, with comparable image quality. However, their application to 3D shapes has been limited to point or voxel representations that can in practice not accurately represent a 3D surface. We propose a diffusion model for neural implicit representations of 3D shapes that operates in the latent space of an auto-decoder. This allows us to generate diverse and high quality 3D surfaces. We additionally show that we can condition our model on images or text to enable image-to-3D generation and text-to-3D generation using CLIP embeddings. Furthermore, adding noise to the latent codes of existing shapes allows us to explore shape variations.

With the wide applications of colored point cloud in many fields, point cloud perceptual quality assessment plays a vital role in the visual communication systems owing to the existence of quality degradations introduced in various stages. However, the existing point cloud quality assessments ignore the mechanism of human visual system (HVS) which has an important impact on the accuracy of the perceptual quality assessment. In this paper, a progressive knowledge transfer based on human visual perception mechanism for perceptual quality assessment of point clouds (PKT-PCQA) is proposed. The PKT-PCQA merges local features from neighboring regions and global features extracted from graph spectrum. Taking into account the HVS properties, the spatial and channel attention mechanism is also considered in PKT-PCQA. Besides, inspired by the hierarchical perception system of human brains, PKT-PCQA adopts a progressive knowledge transfer to convert the coarse-grained quality classification knowledge to the fine-grained quality prediction task. Experiments on three large and independent point cloud assessment datasets show that the proposed no reference PKT-PCQA network achieves better of equivalent performance comparing with the state-of-the-art full reference quality assessment methods, outperforming the existed no reference quality assessment network.