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Automatic segmentation of the organ’s tumor and lesion on biomedical imaging is an essential initiative towards clinical study, treatment planning and digital biomedical research. However, precise tumor segmentation on medical imaging is still an open challenge due to the presence of noise in the imaging sequence, the similar tumor pixel intensity with its neighboring tissues, and heterogeneity among human anatomy. Although most of the state-of-the-art algorithms are architecturally dependent on deep convolution networks (DCNs), like 2D and 3D U-Net, they act as a foundation for many biomedical image segmentation. However, 2D DCNs are incompetent to leverage context information from inter-slice completely. At the same time, 3D DCNs can accumulate inter-slice contextual information over the sizeable receptive texture in the organ, but it consumes a considerable amount of GPU memory and …