用MRI标签辅助CT肿瘤分割，发现无视觉特征则无法有效迁移。
Towards Segmenting the Invisible: An End-to-End Registration and Segmentation Framework for Weakly Supervised Tumour Analysis
- 通过跨模态配准将MRI标签映射到CT，生成伪标签用于弱监督分割
- 健康肝脏分割效果良好（Dice=0.72），但肿瘤区域性能骤降至Dice=0.16
- 揭示了目标病灶在目标模态中无可见特征时，配准无法弥补信息缺失
肝肿瘤消融术面临临床挑战：术前MRI上肿瘤清晰可见，但术中CT因病灶与正常组织对比度低而几乎不可见。本文研究在一种模态（MRI）可见病理、另一种模态（CT）无对应可视特征的场景下，跨模态弱监督的可行性。提出一种融合MSCGUNet进行跨模态图像配准与基于UNet的分割模块的混合框架，实现基于配准的伪标签生成。在CHAOS数据集上，该流程可成功注册并分割健康肝组织，达到0.72的Dice分数；但在包含肿瘤的临床数据上，性能显著下降至0.16，暴露出当前配准方法在目标模态缺乏对应视觉特征时的根本局限。分析表明，尽管可对可见结构进行空间标签传播，但对真正“不可见”的病灶仍难以分割。结果强调，基于配准的标签迁移无法补偿目标模态中判别性特征的缺失，为跨模态医学图像分析研究提供关键启示。
原文摘要 · Abstract (English)
Liver tumour ablation presents a significant clinical challenge: whilst tumours are clearly visible on pre-operative MRI, they are often effectively invisible on intra-operative CT due to minimal contrast between pathological and healthy tissue. This work investigates the feasibility of cross-modality weak supervision for scenarios where pathology is visible in one modality (MRI) but absent in another (CT). We present a hybrid registration-segmentation framework that combines MSCGUNet for inter-modal image registration with a UNet-based segmentation module, enabling registration-assisted pseudo-label generation for CT images. Our evaluation on the CHAOS dataset demonstrates that the pipeline can successfully register and segment healthy liver anatomy, achieving a Dice score of 0.72. However, when applied to clinical data containing tumours, performance degrades substantially (Dice score of 0.16), revealing the fundamental limitations of current registration methods when the target pathology lacks corresponding visual features in the target modality. We analyse the "domain gap" and "feature absence" problems, demonstrating that whilst spatial propagation of labels via registration is feasible for visible structures, segmenting truly invisible pathology remains an open challenge. Our findings highlight that registration-based label transfer cannot compensate for the absence of discriminative features in the target modality, providing important insights for future research in cross-modality medical image analysis. Code an weights are available at: https://github.com/BudhaTronix/Weakly-Supervised-Tumour-Detection
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