UNet++: A Nested U-Net Architecture for Medical Image Segmentation

Zhou, Zongwei; Siddiquee, Mahfuzur Rahman; Tajbakhsh, Nima; Liang, Jianming

doi:10.1007/978-3-030-00889-5_1

Cited by 4,935 publications

(2,845 citation statements)

References 13 publications

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“…RefineNet [70] improves the combination of upsampled representations and the representations of the same resolution copied from the downsample process. Other works include: light upsample process [5], [19], [72], [124], possibly with dilated convolutions used in the backbone [47], [69], [91]; light downsample and heavy upsample processes [115], recombinator networks [40]; improving skip connections with more or complicated convolutional units [48], [89], [143], as well as sending information from low-resolution skip connections to highresolution skip connections [151] or exchanging information between them [34]; studying the details of the upsample process [120]; combining multi-scale pyramid representations [18], [125]; stacking multiple DeconvNets/U-Nets/Hourglass [31], [122] with dense connections [110].…”

Section: Related Workmentioning

confidence: 99%

Deep High-Resolution Representation Learning for Visual Recognition

Wang

Sun

Cheng

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

2,855

1,479

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High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection. Existing state-of-the-art frameworks first encode the input image as a low-resolution representation through a subnetwork that is formed by connecting high-to-low resolution convolutions in series (e.g., ResNet, VGGNet), and then recover the high-resolution representation from the encoded low-resolution representation. Instead, our proposed network, named as High-Resolution Network (HRNet), maintains high-resolution representations through the whole process. There are two key characteristics: (i) Connect the high-to-low resolution convolution streams in parallel; (ii) Repeatedly exchange the information across resolutions. The benefit is that the resulting representation is semantically richer and spatially more precise. We show the superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, suggesting that the HRNet is a stronger backbone for computer vision problems. All the codes are available at https://github.com/HRNet. ! 1 INTRODUCTION D EEP convolutional neural networks (DCNNs) have achieved state-of-the-art results in many computer vision tasks, such as image classification, object detection, semantic segmentation, human pose estimation, and so on. The strength is that DCNNs are able to learn richer representations than conventional hand-crafted representations. Most recently-developed classification networks, including AlexNet [59], VGGNet [101], GoogleNet [108], ResNet [39], etc., follow the design rule of LeNet-5 [61]. This is depicted in Figure 1 (a): gradually reduce the spatial size of the feature maps, connect the convolutions from high resolution to low resolution in series, and lead to a low-resolution representation, which is further processed for classification.High-resolution representations are needed for positionsensitive tasks, e.g., semantic segmentation, human pose estimation, and object detection. The previous state-of-the-art methods adopt the high-resolution recovery process to raise the representation resolution from the low-resolution representation outputted by a classification or classification-like network as depicted in Figure 1 (b), e.g., Hourglass [83], Seg-Net [3], DeconvNet [85], U-Net [95], SimpleBaseline [124], and encoder-decoder [90]. In addition, dilated convolutions are used to remove some down-sample layers and thus yield medium-resolution representations [15], [144].We present a novel architecture, namely High-Resolution Net (HRNet), which is able to maintain high-resolution representations through the whole process. We start from a highresolution convolution stream, gradually add high-to-low resolution convolution streams one by one, and connect the multi-resolution streams in parallel. The resulting network • J. Wang is with Microsoft Research,

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Section: Related Workmentioning

confidence: 99%

Deep High-Resolution Representation Learning for Visual Recognition

Wang

Sun

Cheng

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

2,855

1,479

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“…Ronneberger et al [5] modified and extended the FCN architecture to an U-Net architecture. There are various modification and extension based on U-Net architecture [4], [6], [17], [19]- [22] to achieve better segmentation results on both natural images and biomedical images.…”

Section: Related Workmentioning

confidence: 99%

ResUNet++: An Advanced Architecture for Medical Image Segmentation

Jha

Smedsrud

Riegler

et al. 2019

2019 IEEE International Symposium on Multimedia (ISM)

738

330

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Accurate computer-aided polyp detection and segmentation during colonoscopy examinations can help endoscopists resect abnormal tissue and thereby decrease chances of polyps growing into cancer. Towards developing a fully automated model for pixel-wise polyp segmentation, we propose ResUNet++, which is an improved ResUNet architecture for colonoscopic image segmentation. Our experimental evaluations show that the suggested architecture produces good segmentation results on publicly available datasets. Furthermore, ResUNet++ significantly outperforms U-Net and ResUNet, two key state-ofthe-art deep learning architectures, by achieving high evaluation scores with a dice coefficient of 81.33%, and a mean Intersection over Union (mIoU) of 79.27% for the Kvasir-SEG dataset and a dice coefficient of 79.55%, and a mIoU of 79.62% with CVC-612 dataset.

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“…The selection of a deep learning or convolutional neural network model is the most important step in a medical image segmentation pipeline. There is a variety of model architectures and each has different strengths and weaknesses [7,8,10,11,[23][24][25][26][27][28][29].…”

Section: Model Architecturementioning

confidence: 99%

MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning

Müller

2019

Submissions to the 2019 Kidney Tumor Segmentation Challenge: KiTS19

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The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. Still, current image segmentation platforms do not provide the required functionalities for plain setup of medical image segmentation pipelines. Already implemented pipelines are commonly standalone software, optimized on a specific public data set. Therefore, this paper introduces the open-source Python library MIScnn. The aim of MIScnn is to provide an intuitive API allowing fast building of medical image segmentation pipelines including data I/O, preprocessing, data augmentation, patch-wise analysis, metrics, a library with state-of-the-art deep learning models and model utilization like training, prediction, as well as fully automatic evaluation (e.g. cross-validation). Similarly, high configurability and multiple open interfaces allow full pipeline customization. Running a crossvalidation with MIScnn on the Kidney Tumor Segmentation Challenge 2019 data set (multi-class semantic segmentation with 300 CT scans) resulted into a powerful predictor based on the standard 3D U-Net model. With this experiment, we could show that the MIScnn framework enables researchers to rapidly set up a complete medical image segmentation pipeline by using just a few lines of code. The source code for MIScnn is available in the Git repository: https://github.com/frankkramer-lab/MIScnn.

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UNet++: A Nested U-Net Architecture for Medical Image Segmentation

Cited by 4,935 publications

References 13 publications

Deep High-Resolution Representation Learning for Visual Recognition

Deep High-Resolution Representation Learning for Visual Recognition

ResUNet++: An Advanced Architecture for Medical Image Segmentation

MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning

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