Network measurement provides the network designers and managers with fine-grained information on the operational statuses of the network and is the basis for efficient network management and optimization. Network tomography is a hot topic in the field of network measurement and is an end-to-end approach for network measurement. Unlike the traditional internal approaches for network measurement, network tomography uses the end-to-end measurements to infer the internal network performance and network states, thereby incurring low overhead to achieve the network measurement that is independent of the network composition and the network protocols. This paper systematically summarizes the representative research works about network tomography in the past few years. First, the basic model of network tomography is given and three key factors that impact the performance of network tomography are identified: the monitoring node placement, the measurement path construction, and the measurement data analysis. Then, the related works are reviewed on these three factors separately. In particular, the major limitations of existing network tomography methods in practical applications are explored, and the efficient solutions proposed in recent years are introduced. Lastly, some challenges and future research directions are discussed in the field of network tomography based on existing research works.