Magnetic Resonance Imaging (MRI) is pivotal in radiology, offering
non-invasive and high-quality insights into the human body. Precise
segmentation of MRIs into different organs and tissues would be highly
beneficial since it would allow for a higher level of understanding of the
image content and enable important measurements, which are essential for
accurate diagnosis and effective treatment planning. Specifically, segmenting
bones in MRI would allow for more quantitative assessments of musculoskeletal
conditions, while such assessments are largely absent in current radiological
practice. The difficulty of bone MRI segmentation is illustrated by the fact
that limited algorithms are publicly available for use, and those contained in
the literature typically address a specific anatomic area. In our study, we
propose a versatile, publicly available deep-learning model for bone
segmentation in MRI across multiple standard MRI locations. The proposed model
can operate in two modes: fully automated segmentation and prompt-based
segmentation. Our contributions include (1) collecting and annotating a new MRI
dataset across various MRI protocols, encompassing over 300 annotated volumes
and 8485 annotated slices across diverse anatomic regions; (2) investigating
several standard network architectures and strategies for automated
segmentation; (3) introducing SegmentAnyBone, an innovative foundational
model-based approach that extends Segment Anything Model (SAM); (4) comparative
analysis of our algorithm and previous approaches; and (5) generalization
analysis of our algorithm across different anatomical locations and MRI
sequences, as well as an external dataset. We publicly release our model at
this https URL

我们提出了一种通用的深度学习模型，用于对 MRI 骨骼进行分割，可在多个标准 MRI 位置进行全自动分割，并提供提示式分割。

SegmentAnyBone：MRI 上任意位置通用骨骼分割模型

SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location  on MRI

Accessing high-quality video content can be challenging due to insufficient
and unstable network bandwidth. Recent advances in neural enhancement have
shown promising results in improving the quality of degraded videos through
deep learning. Neural-Enhanced Streaming (NES) incorporates this new approach
into video streaming, allowing users to download low-quality video segments and
then enhance them to obtain high-quality content without violating the playback
of the video stream. We introduce BONES, an NES control algorithm that jointly
manages the network and computational resources to maximize the quality of
experience (QoE) of the user. BONES formulates NES as a Lyapunov optimization
problem and solves it in an online manner with near-optimal performance, making
it the first NES algorithm to provide a theoretical performance guarantee. Our
comprehensive experimental results indicate that BONES increases QoE by 4% to
13% over state-of-the-art algorithms, demonstrating its potential to enhance
the video streaming experience for users. Our code and data will be released to
the public.

通过深度学习改善视频质量的神经增强对视频流媒体进行了探索，并引入了一种名为 BONES 的控制算法，以最大化用户体验的质量。实验证明相比最先进的算法，BONES 能够提高用户的体验质量（QoE）4％至 13％。