Segmenting the boundary between tumor and healthy tissue during surgical cancer resection poses a significant challenge. In recent years, Hyperspectral Imaging (HSI) combined with Machine Learning (ML) has emerged as a promising solution. However, due to the extensive information contained within the spectral domain, most ML approaches primarily classify individual HSI (super-)pixels, or tiles, without taking into account their spatial context. In this paper, we propose an improved methodology that leverages the spatial context of tiles for more robust and smoother segmentation. To address the irregular shapes of tiles, we utilize Graph Neural Networks (GNNs) to propagate context information across neighboring regions. The features for each tile within the graph are extracted using a Convolutional Neural Network (CNN), which is trained simultaneously with the subsequent GNN. Moreover, we incorporate local image quality metrics into the loss function to enhance the training procedure's robustness against low-quality regions in the training images. We demonstrate the superiority of our proposed method using a clinical ex vivo dataset consisting of 51 HSI images from 30 patients. Despite the limited dataset, the GNN-based model significantly outperforms context-agnostic approaches, accurately distinguishing between healthy and tumor tissues, even in images from previously unseen patients. Furthermore, we show that our carefully designed loss function, accounting for local image quality, results in additional improvements. Our findings demonstrate that context-aware GNN algorithms can robustly find tumor demarcations on HSI images, ultimately contributing to better surgery success and patient outcome.

翻译：在肿瘤手术切除过程中，精准分割肿瘤与健康组织的边界是一项重大挑战。近年来，高光谱成像结合机器学习已成为一种颇具前景的解决方案。然而，由于光谱域内包含海量信息，大多数机器学习方法主要对单个高光谱像素或超像素块进行分类，而未能充分利用其空间上下文信息。本文提出一种改进方法，通过利用图像块的空间上下文实现更鲁棒且更平滑的分割。为解决图像块形状不规则的问题，我们采用图神经网络在相邻区域间传播上下文信息。图结构中各图像块的特征通过卷积神经网络提取，该网络与后续的图神经网络同步训练。此外，我们将局部图像质量指标融入损失函数，以增强训练过程对低质量图像区域的鲁棒性。通过包含来自30位患者的51张高光谱图像的临床离体数据集，我们验证了所提方法的优越性。尽管数据集规模有限，基于图神经网络的模型仍显著优于忽略上下文信息的方法，即使在面对既往未见过患者的图像时，也能准确区分健康组织与肿瘤组织。进一步研究表明，我们设计的考虑局部图像质量的损失函数能带来额外的性能提升。实验结果表明，上下文感知的图神经网络算法能够在高光谱图像上鲁棒地识别肿瘤边界，最终助力提升手术成功率与患者预后。