Positron emission tomography (PET) combined with computed tomography (CT)
imaging is routinely used in cancer diagnosis and prognosis by providing
complementary information. Automatically segmenting tumors in PET/CT images can
significantly improve examination efficiency. Traditional multi-modal
segmentation solutions mainly rely on concatenation operations for modality
fusion, which fail to effectively model the non-linear dependencies between PET
and CT modalities. Recent studies have investigated various approaches to
optimize the fusion of modality-specific features for enhancing joint
representations. However, modality-specific encoders used in these methods
operate independently, inadequately leveraging the synergistic relationships
inherent in PET and CT modalities, for example, the complementarity between
semantics and structure. To address these issues, we propose a Hierarchical
Adaptive Interaction and Weighting Network termed H2ASeg to explore the
intrinsic cross-modal correlations and transfer potential complementary
information. Specifically, we design a Modality-Cooperative Spatial Attention
(MCSA) module that performs intra- and inter-modal interactions globally and
locally. Additionally, a Target-Aware Modality Weighting (TAMW) module is
developed to highlight tumor-related features within multi-modal features,
thereby refining tumor segmentation. By embedding these modules across
different layers, H2ASeg can hierarchically model cross-modal correlations,
enabling a nuanced understanding of both semantic and structural tumor
features. Extensive experiments demonstrate the superiority of H2ASeg,
outperforming state-of-the-art methods on AutoPet-II and Hecktor2022
benchmarks. The code is released at this https URL

通过 Hierarchical Adaptive Interaction and Weighting Network（H2ASeg）来积极地探索非性质语义与结构之间的互补关系，有效地模型化用于 PET 和 CT 模式间非线性关联，进而提高肿瘤分割的准确性。在 AutoPet-II 和 Hecktor2022 基准测试中，H2ASeg 表现优于现有方法。

H2ASeg: PET/CT 图像中肿瘤分割的分层自适应交互和加权网络

H2ASeg: Hierarchical Adaptive Interaction and Weighting Network for  Tumor Segmentation in PET/CT Images

Efficient RGB-D semantic segmentation has received considerable attention in
mobile robots, which plays a vital role in analyzing and recognizing
environmental information. According to previous studies, depth information can
provide corresponding geometric relationships for objects and scenes, but
actual depth data usually exist as noise. To avoid unfavorable effects on
segmentation accuracy and computation, it is necessary to design an efficient
framework to leverage cross-modal correlations and complementary cues. In this
paper, we propose an efficient lightweight encoder-decoder network that reduces
the computational parameters and guarantees the robustness of the algorithm.
Working with channel and spatial fusion attention modules, our network
effectively captures multi-level RGB-D features. A globally guided local
affinity context module is proposed to obtain sufficient high-level context
information. The decoder utilizes a lightweight residual unit that combines
short- and long-distance information with a few redundant computations.
Experimental results on NYUv2, SUN RGB-D, and Cityscapes datasets show that our
method achieves a better trade-off among segmentation accuracy, inference time,
and parameters than the state-of-the-art methods. The source code will be at
this https URL

提出了一种有效的轻量级编码器 - 解码器网络以及其他相关模块，用于在移动机器人中进行 RGB-D 语义分割，通过跨模态相关性和补充线索可以准确提取多层次 RGB-D 特征，实验结果表明该方法在分割准确性、推理时间和参数方面具有更好的权衡。