Accurate and automated segmentation of intervertebral discs (IVDs) in medical
images is crucial for assessing spine-related disorders, such as osteoporosis,
vertebral fractures, or IVD herniation. We present HCA-Net, a novel contextual
attention network architecture for semantic labeling of IVDs, with a special
focus on exploiting prior geometric information. Our approach excels at
processing features across different scales and effectively consolidating them
to capture the intricate spatial relationships within the spinal cord. To
achieve this, HCA-Net models IVD labeling as a pose estimation problem, aiming
to minimize the discrepancy between each predicted IVD location and its
corresponding actual joint location. In addition, we introduce a skeletal loss
term to reinforce the model's geometric dependence on the spine. This loss
function is designed to constrain the model's predictions to a range that
matches the general structure of the human vertebral skeleton. As a result, the
network learns to reduce the occurrence of false predictions and adaptively
improves the accuracy of IVD location estimation. Through extensive
experimental evaluation on multi-center spine datasets, our approach
consistently outperforms previous state-of-the-art methods on both MRI T1w and
T2w modalities. The codebase is accessible to the public on
\href{this https URL}{GitHub}.

通过 HCA-Net 体系结构，我们提出了一种新的语境注意力网络来进行椎间盘（IVD）的语义标注，特别关注利用先前的几何信息。通过将 IVD 标记作为姿势估计问题，我们的方法通过最小化每个预测 IVD 位置与其相应的实际关节位置之间的差异来优化准确性。通过在多个中心脊柱数据集上进行广泛的实验评估，我们的方法在 MRI T1w 和 T2w 模态上始终优于先前的最先进方法。

HCA-Net：用于椎间盘语义标记的分层上下文注意力网络

HCA-Net: Hierarchical Context Attention Network for Intervertebral Disc  Semantic Labeling

Contexts play an important role in the saliency detection task. However,
given a context region, not all contextual information is helpful for the final
task. In this paper, we propose a novel pixel-wise contextual attention
network, i.e., the PiCANet, to learn to selectively attend to informative
context locations for each pixel. Specifically, for each pixel, it can generate
an attention map in which each attention weight corresponds to the contextual
relevance at each context location. An attended contextual feature can then be
constructed by selectively aggregating the contextual information. We formulate
the proposed PiCANet in both global and local forms to attend to global and
local contexts, respectively. Both models are fully differentiable and can be
embedded into CNNs for joint training. We also incorporate the proposed models
with the U-Net architecture to detect salient objects. Extensive experiments
show that the proposed PiCANets can consistently improve saliency detection
performance. The global and local PiCANets facilitate learning global contrast
and homogeneousness, respectively. As a result, our saliency model can detect
salient objects more accurately and uniformly, thus performing favorably
against the state-of-the-art methods.

本文提出了一种像素级的上下文注意力网络 ——PiCANet，旨在学习选择性地注意每个像素的信息上下文位置，通过全局和局部形式，构建所关注的上下文特征，与 U-Net 结构相结合，可以准确且均匀地检测显著对象，在显著性检测性能方面表现优异。