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Dense Pyramid Context Encoder-Decoder Network for Kidney Lesion Segmentation

In this manuscript, an automated solution is presented for the kidney lesion segmentation. The proposed method consists of two-stage learning procedures which generating prediction masks for kidney and lesion respectively. Since we adopt 2D axial images from CT scans as evaluation data, it is critical to extract sufficient contextual information for capturing the objects varied significantly in appearance within different slices. Hence, we redesign an encoderdecoder network for more effective feature representations learning. We evaluate our method on 2019 Kidney Tumor Segmentation Challenge. There are total 210 labeled CT scans released as training and validation data. The source code can be found at: https://github.com/Zakiyi/kits_2019_segmentation_challenge.