In this study, we present a segmentation algorithm based on ray casting and border point detection. The algorithm’s main parameter is the number of emitted rays, which defines the resolution of the object’s boundary. The value of this parameter depends on the shape of the target region. For instance, 8 rays are enough to segment the left ventricle with the average Dice similarity coefficient approximately equal to 85%. Having gathered the data of rays, the training datasets had a relatively high level of class imbalance (up to 90%). To cope with this issue, ensemble-based classifiers used to manage imbalanced datasets such as AdaBoost.M2, RUSBoost, UnderBagging, SMOTEBagging, SMOTEBoost were used for border detection. For estimation of the accuracy and processing time, the proposed algorithm used a cardiac MRI dataset of the University of York and brain tumour dataset of Southern Medical University. The highest Dice similarity coefficients for the heart and brain tumour segmentation, equal to 86.5±6.9% and 89.5±6.7%, respectively, were achieved by the proposed algorithm. The segmentation time of a cardiac frame equals 4.1±2.3 ms and 20.2±23.6 ms for 8 and 64 rays, respectively. Brain tumour segmentation took 5.1±1.1 ms and 16.0±3.0 ms for 8 and 64 rays respectively. By testing the different medical imaging cases, the proposed algorithm is not time-consuming and highly accurate for convex and closed objects. The scalability of the algorithm allows implementing different border detection techniques working in parallel.