Abstract
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: this https URL
Abstract (translated)
肝脏肿瘤消融在临床上是一个重大的挑战：尽管术前MRI图像上可以清晰地看到肿瘤，但在术中CT图像上由于病理组织和健康组织之间的对比度极低，这些肿瘤往往变得几乎不可见。这项研究探讨了跨模式弱监督方法的可行性，在这种方法中，一种成像方式（如MRI）能显示病理性结构，而另一种成像方式（如CT）则无法显示。我们提出了一种结合MSCGUNet进行跨模态图像配准与基于UNet的分割模块的混合框架，该框架能够生成注册辅助伪标签以供CT图像使用。 我们在CHAOS数据集上的评估表明，该流程可以成功地对健康肝脏解剖结构进行配准和分割，达到Dice分数为0.72的成绩。然而，在包含肿瘤的实际临床数据中应用时，性能显著下降（Dice分数为0.16），这揭示了现有注册方法在目标病理性特征缺乏相应视觉特性时的固有限制。 我们分析了“领域差距”和“特征缺失”的问题，表明尽管通过配准可以在可视结构间传播标签，但分割真正不可见的病理仍然是一个开放性的挑战。我们的研究结果强调，基于注册的标签转移不能弥补目标模态中缺乏区分性特征的问题，并为未来跨模式医学图像分析的研究提供了重要的见解。 代码和权重可在以下网址获得：[链接]（此处应插入具体URL）
URL
https://arxiv.org/abs/2602.05453
