Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Taihe Hospital, Hubei University of Medicine, Shiyan, China.
BMC Med Imaging. 2024 May 31;24(1):129. doi: 10.1186/s12880-024-01309-1.
Segmenting liver vessels from contrast-enhanced computed tomography images is essential for diagnosing liver diseases, planning surgeries and delivering radiotherapy. Nevertheless, identifying vessels is a challenging task due to the tiny cross-sectional areas occupied by vessels, which has posed great challenges for vessel segmentation, such as limited features to be learned and difficult to construct high-quality as well as large-volume data.
We present an approach that only requires a few labeled vessels but delivers significantly improved results. Our model starts with vessel enhancement by fading out liver intensity and generates candidate vessels by a classifier fed with a large number of image filters. Afterwards, the initial segmentation is refined using Markov random fields.
In experiments on the well-known dataset 3D-IRCADb, the averaged Dice coefficient is lifted to 0.63, and the mean sensitivity is increased to 0.71. These results are significantly better than those obtained from existing machine-learning approaches and comparable to those generated from deep-learning models.
Sophisticated integration of a large number of filters is able to pinpoint effective features from liver images that are sufficient to distinguish vessels from other liver tissues under a scarcity of large-volume labeled data. The study can shed light on medical image segmentation, especially for those without sufficient data.
从增强计算机断层扫描图像中分割肝脏血管对于诊断肝脏疾病、规划手术和提供放射治疗至关重要。然而，由于血管占据的横截面非常小，因此识别血管是一项具有挑战性的任务，这给血管分割带来了很大的困难，例如可学习的特征有限，难以构建高质量和大容量的数据。
我们提出了一种仅需要少量标记血管但能显著提高分割效果的方法。我们的模型首先通过淡化肝脏强度来增强血管，然后通过一个分类器用大量图像滤波器生成候选血管。之后，使用马尔可夫随机场对初始分割进行细化。
在著名的 3D-IRCADb 数据集上的实验中，平均 Dice 系数提高到 0.63，平均灵敏度提高到 0.71。这些结果明显优于现有机器学习方法的结果，与深度学习模型生成的结果相当。
大量滤波器的复杂集成能够从肝脏图像中精确定位有效的特征，这些特征足以在缺乏大量标记数据的情况下区分血管和其他肝脏组织。该研究可为医学图像分割提供启示，特别是对于那些数据不足的情况。
