The burden of liver tumors is important, ranking as the fourth leading cause of cancer mortality. In case of hepatocellular carcinoma (HCC), the delineation of liver and tumor on contrast-enhanced magnetic resonance imaging (CE-MRI) is performed to guide the treatment strategy. As this task is time-consuming, needs high expertise and could be subject to inter-observer variability there is a strong need for automatic tools. However, challenges arise from the lack of available training data, as well as the high variability in terms of image resolution and MRI sequence. In this work we propose to compare two different pipelines based on anisotropic models to obtain the segmentation of the liver and tumors. The first pipeline corresponds to a baseline multi-class model that performs the simultaneous segmentation of the liver and tumor classes. In the second approach, we train two distinct binary models, one segmenting the liver only and the other the tumors. Our results show that both pipelines exhibit different strengths and weaknesses. Moreover we propose an uncertainty quantification strategy allowing the identification of potential false positive tumor lesions. Both solutions were submitted to the MICCAI 2023 Atlas challenge regarding liver and tumor segmentation.

翻译：肝脏肿瘤的负担重大，是癌症死亡的第四大原因。对于肝细胞癌（HCC），在对比增强磁共振成像（CE-MRI）上进行肝脏和肿瘤的勾画，以指导治疗策略。由于该任务耗时、需要高度专业知识且可能存在观察者间变异性，因此亟需自动化工具。然而，挑战来自训练数据的缺乏，以及图像分辨率和MRI序列的高度变异性。在本研究中，我们提出比较两种基于各向异性模型的不同流程，以获得肝脏和肿瘤的分割。第一个流程对应于一个基线多类模型，同时进行肝脏和肿瘤类别的分割。在第二种方法中，我们训练两个独立的二值模型，一个仅分割肝脏，另一个仅分割肿瘤。我们的结果表明，两种流程展现出不同的优势和劣势。此外，我们提出了一种不确定性量化策略，用于识别潜在的假阳性肿瘤病灶。两种解决方案均已提交至关于肝脏和肿瘤分割的MICCAI 2023 Atlas挑战赛。