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Due to its excellent performance, U-Net is the most widely used backbonearchitecture for biomedical image segmentation in the recent years. However, inour studies, we observe that there is a considerable performance drop in thecase of detecting smaller anatomical landmarks with blurred noisy boundaries.We analyze this issue in detail, and address it by proposing an over-completearchitecture (Ki-Net) which involves projecting the data onto higher dimensions(in the spatial sense). This network, when augmented with U-Net, results insignificant improvements in the case of segmenting small anatomical landmarksand blurred noisy boundaries while obtaining better overall performance.Furthermore, the proposed network has additional benefits like fasterconvergence and fewer number of parameters. We evaluate the proposed method onthe task of brain anatomy segmentation from 2D Ultrasound (US) of pretermneonates, and achieve an improvement of around 4% in terms of the DICE accuracyand Jaccard index as compared to the standard-U-Net, while outperforming therecent best methods by 2%. Code:https://github.com/jeya-maria-jose/KiU-Net-pytorch .

KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representations