Institute for Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Austria.
BioTechMed, Krenngasse 37/1, 8010, Graz, Austria.
Sci Rep. 2017 Apr 18;7(1):892. doi: 10.1038/s41598-017-00940-z.
Ultrasound (US) is the most commonly used liver imaging modality worldwide. Due to its low cost, it is increasingly used in the follow-up of cancer patients with metastases localized in the liver. In this contribution, we present the results of an interactive segmentation approach for liver metastases in US acquisitions. A (semi-) automatic segmentation is still very challenging because of the low image quality and the low contrast between the metastasis and the surrounding liver tissue. Thus, the state of the art in clinical practice is still manual measurement and outlining of the metastases in the US images. We tackle the problem by providing an interactive segmentation approach providing real-time feedback of the segmentation results. The approach has been evaluated with typical US acquisitions from the clinical routine, and the datasets consisted of pancreatic cancer metastases. Even for difficult cases, satisfying segmentations results could be achieved because of the interactive real-time behavior of the approach. In total, 40 clinical images have been evaluated with our method by comparing the results against manual ground truth segmentations. This evaluation yielded to an average Dice Score of 85% and an average Hausdorff Distance of 13 pixels.
超声（US）是全球最常用的肝脏成像方式。由于其成本低，因此越来越多地用于随访肝脏局部转移的癌症患者。在本贡献中，我们展示了一种用于 US 采集的肝转移交互式分割方法的结果。由于图像质量低和转移与周围肝组织之间的对比度低，（半）自动分割仍然具有很大的挑战性。因此，临床实践中的最新技术仍然是手动测量和勾勒 US 图像中的转移。我们通过提供一种交互式分割方法来解决该问题，该方法实时反馈分割结果。该方法使用来自临床常规的典型 US 采集进行了评估，数据集由胰腺癌转移组成。即使对于困难的病例，由于该方法的交互式实时行为，也可以实现令人满意的分割结果。总共使用我们的方法评估了 40 个临床图像，通过将结果与手动地面实况分割进行比较。该评估得到了 85％的平均 Dice 得分和 13 像素的平均 Hausdorff 距离。
