Volume estimation plays an important role in clinical diagnosis. For example, cardiac ventricular volumes
including left ventricle (LV) and right ventricle (RV) are important clinical indicators of cardiac functions.
Accurate and automatic estimation of the ventricular volumes is essential to the assessment of cardiac functions
and diagnosis of heart diseases. Conventional methods are dependent on an intermediate segmentation step
which is obtained either manually or automatically. However, manual segmentation is extremely time-consuming,
subjective and highly non-reproducible; automatic segmentation is still challenging, computationally expensive,
and completely unsolved for the RV.
Towards accurate and efficient direct volume estimation, our group has been researching on learning based
methods without segmentation by leveraging state-of-the-art machine learning techniques. Our direct estimation
methods remove the accessional step of segmentation and can naturally deal with various volume estimation tasks.
Moreover, they are extremely flexible to be used for volume estimation of either joint bi-ventricles (LV and RV)
or individual LV/RV. We comparatively study the performance of direct methods on cardiac ventricular volume
estimation by comparing with segmentation based methods. Experimental results show that direct estimation
methods provide more accurate estimation of cardiac ventricular volumes than segmentation based methods.
This indicates that direct estimation methods not only provide a convenient and mature clinical tool for cardiac
volume estimation but also enables diagnosis of cardiac diseases to be conducted in a more efficient and reliable