Nuclei Segmentation with Recurrent Residual Convolutional Neural Networks based U-Net (R2U-Net)

By Md. Zahangir Alom, C. Yakopcic, Tarek M. Taha, Vijayan K. Asari. National Aerospace and Electronics Conference, 2018

329 citations recorded by the source corpus.

Bio-medical image segmentation is one of the promising sectors where nuclei segmentation from high-resolution histopathological images enables extraction of very high-quality features for nuclear morphometrics and other analysis metrics in the field of digital pathology. The traditional methods including Otsu thresholding and watershed methods do not work properly in different challenging cases. However, Deep Learning (DL) based approaches are showing tremendous success in different modalities of bio-medical imaging including computation pathology. Recently, the Recurrent Residual U-Net (R2U-Net) has been proposed, which has shown state-of-the-art (SOTA) performance in different modalities (retinal blood vessel, skin cancer, and lung segmentation) in medical image segmentation. However, in this implementation, the R2U-Net is applied to nuclei segmentation for the first time on a publicly available dataset that was collected from the Data Science Bowl Grand Challenge in 2018. The R2U-Net shows around 92.15% segmentation accuracy in terms of the Dice Coefficient (DC) during the testing phase. In addition, the qualitative results show accurate segmentation, which clearly demonstrates the robustness of the R2U-Net model for the nuclei segmentation task.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d6785c954cc5562838a57e185e99d0496b5fd5a2

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Citations in the corpus

RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans (2018, methodology): “The soft mask branch has an encoder-decoder structure which has been widely applied to medical image segmentation (Ronneberger et al., 2015; Çiçek et al., 2016; Alom et al., 2018).”
U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications (2020, methodology): “[52], [66] devised a U-net model containing both recurrent connections and residual connections.”
TransAttUnet: Multi-Level Attention-Guided U-Net With Transformer for Medical Image Segmentation (2021, methodology): “…experiments include three broad approaches as baselines, including attention-guided models such as Attention U-Net [10], Attention R2U-Net [38], Channel-UNet [8], XLSor [23], FANet [39], and PraNet [40], which have been proposed to improve segmentation performance by incorporating…”