Computed tomography (CT) images can provide a view of the patients' internal organs. This property is particular conducive to surgery planning. As one of the most common cancers, renal cancer also can be treated effectively by the laparo-scopic partial nephrectomy (LPN). However, automatic and accurate kidney and renal tumor seg-mentation in CT images remains a challenge. In this paper, BiSC-UNet framework which combines two different resolution SC-UNets is proposed for kidney and renal tumor fine segmentation. Rough SC-UNet is in charge of locating the kidney and renal tumor roughly to achieve the kidney region of interest (ROI) in original CT images. The other fine SC-UNet utilizes the kidney ROI for final fine kidney and renal tumor segmentation. In the proposed SC-UNet, not only the labeled kidney and renal tumor are used for training, but also the labeled renal tumor edge and kidney edge are utilized for accurate segmentation result. A balanced cross-entropy which selects and dynamic weights different regions and categorys is proposed for category imbalance. Extensive experiments on CT datasets demonstrate the effectiveness of proposed network.