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

Temel Kavramlar

Proposing H2ASeg for precise PET/CT tumor segmentation by hierarchically modeling cross-modal correlations.

Özet

PET/CT imaging combines metabolic and structural information for cancer diagnosis.
Automatic tumor segmentation in PET/CT images enhances examination efficiency.
Traditional methods fail to effectively model non-linear dependencies between PET and CT modalities.
H2ASeg introduces MCSA and TAMW modules for improved tumor segmentation.
Extensive experiments show H2ASeg outperforms state-of-the-art methods.

İstatistikler

"Extensive experiments demonstrate the superiority of H2ASeg, outperforming state-of-the-art methods on AutoPet-II and Hecktor2022 benchmarks."

Alıntılar

"We propose a Hierarchical Adaptive Interaction and Weighting Network termed H2ASeg to explore the intrinsic cross-modal correlations and transfer potential complementary information."

Önemli Bilgiler Şuradan Elde Edildi

H2ASeg

by Jinpeng Lu,J... : arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18339.pdf

Daha Derin Sorular

How can the hierarchical interaction of PET and CT images improve tumor localization?

The hierarchical interaction of PET and CT images in deep learning networks, as demonstrated in the H2ASeg model, plays a crucial role in improving tumor localization. By hierarchically modeling the correlations between PET and CT modalities, the network can effectively exploit the complementary information present in both imaging modalities. PET images provide sensitivity to high metabolic areas, aiding in localizing tumors, while CT images offer detailed structural information. The hierarchical interaction allows the network to capture both semantic and structural features of tumors, leading to more precise segmentation. This approach mimics how radiologists analyze PET/CT images, first roughly locating tumor regions using PET and then using detailed CT information for accurate boundary delineation. Through this hierarchical interaction, the network can achieve a nuanced understanding of tumor features, enhancing localization accuracy.

What are the limitations of traditional multi-modal segmentation methods?

Traditional multi-modal segmentation methods often rely on concatenation operations for modality fusion, which can be limiting in effectively modeling the non-linear dependencies between different modalities such as PET and CT. These methods may struggle to capture the synergistic relationships between modalities, leading to suboptimal segmentation results. Additionally, modality-specific encoders in traditional methods operate independently, which hinders the exploration of the synergistic relationships inherent in PET and CT modalities. This independence of modality-specific encoders can pose challenges in leveraging the complementarity between semantics and structure offered by PET and CT images. As a result, traditional methods may not fully exploit the potential of multi-modal imaging for tumor segmentation.

How can the synergistic relationships between PET and CT modalities be further leveraged for tumor segmentation?

To further leverage the synergistic relationships between PET and CT modalities for tumor segmentation, advanced approaches like the H2ASeg model introduce innovative modules such as Modality-Cooperative Spatial Attention (MCSA) and Target-Aware Modality Weighting (TAMW). These modules enable the network to interact across modalities at different levels, capturing complementary information globally and locally. The MCSA module facilitates the transfer of valuable information between PET and CT images, enhancing feature representation. On the other hand, the TAMW module focuses on highlighting tumor-related features within multi-modal features, refining segmentation results. By embedding these modules in the network architecture, the synergistic relationships between PET and CT modalities can be effectively utilized to improve tumor segmentation accuracy.