Skwirczyński Maciej, Tabor Zbisław, Lasek Julia, Schneider Zofia, Gibała Sebastian, Kucybała Iwona, Urbanik Andrzej, Obuchowicz Rafał
Faculty of Mathematics and Computer Science, Jagiellonian University, 30-348 Krakow, Poland.
Faculty of Electrical Engineering, Automatics, Computer Science, and Biomedical Engineering, AGH University of Science and Technology, 30-059 Krakow, Poland.
Cancers (Basel). 2023 Jun 11;15(12):3142. doi: 10.3390/cancers15123142.
The problems in diagnosing the state of a vital organ such as the liver are complex and remain unresolved. These problems are underscored by frequently published studies on this issue. At the same time, demand for imaging diagnostics, preferably using a method that can detect the disease at the earliest possible stage, is constantly increasing. In this paper, we present liver diseases in the context of diagnosis, diagnostic problems, and possible elimination. We discuss the dataset and methods and present the stages of the pipeline we developed, leading to multiclass segmentation of the liver in multiparametric MR image into lesions and normal tissue. Finally, based on the processing results, each case is classified as either a healthy liver or a liver with lesions. For the training set, the AUC ROC is 0.925 (standard error 0.013 and a -value less than 0.001), and for the test set, the AUC ROC is 0.852 (standard error 0.039 and a -value less than 0.001). Further refinements to the proposed pipeline are also discussed. The proposed approach could be used in the detection of focal lesions in the liver and the description of liver tumors. Practical application of the developed multi-class segmentation method represents a key step toward standardizing the medical evaluation of focal lesions in the liver.
诊断肝脏等重要器官的状态存在复杂问题且尚未解决。关于此问题的频繁发表的研究凸显了这些问题。与此同时，对成像诊断的需求不断增加，最好采用能够在尽可能早的阶段检测出疾病的方法。在本文中，我们从诊断、诊断问题及可能的解决方法等方面介绍肝脏疾病。我们讨论了数据集和方法，并展示了我们开发的流程阶段，该流程可将多参数磁共振图像中的肝脏进行多类别分割，分为病变组织和正常组织。最后，根据处理结果，将每个病例分类为健康肝脏或有病变的肝脏。对于训练集，AUC ROC为0.925（标准误差0.013且p值小于0.001），对于测试集，AUC ROC为0.852（标准误差0.039且p值小于0.001）。还讨论了对所提出流程的进一步优化。所提出的方法可用于检测肝脏中的局灶性病变以及描述肝脏肿瘤。所开发的多类别分割方法的实际应用是朝着标准化肝脏局灶性病变医学评估迈出的关键一步。
