VinDr-Multiphase: A Benchmark Dataset for Phase Recognition in Abdominal Contrast-Enhanced CT Scans
This dataset was used to develop and validate our proposed method for phase recognition in abdominal contrast-enhanced CT scans using deep learning and random sampling.
Figure 1: Visual differences between the Non-Contrast (NC), Arterial (A), and Venous (V) phases in CT scans. The radiation enhancement in the different phases helps to detect different lesions in CT scans such as metastases, central tumor necrosis, and other pathologies. Radiologists usually look at arteries or veins and parenchyma to distinguish the phases.
Table 1. Characteristics of patients in the training and test datasets.
For any publication that explores this resource, the authors must cite this original paper:
Binh T. Dao, Thang V. Nguyen, Hieu H. Pham, and Ha Q. Nguyen, “Phase Recognition in Contrast-Enhanced CT Scans based on Deep Learning and Random Sampling,” Medical Physics, to appear.