An Improved Framework Called Du++ Applied to Brain Tumor Segmentation

Chen, Fujuan; Ding, Yi; Wu, Zhixing; Wu, Dongyuan; Wen, Jin-Mei

doi:10.1109/iccwamtip.2018.8632559

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…We first presented UNet++ in our DLMIA 2018 paper [51]. UNet++ has since been quickly adopted by the research community, either as a strong baseline for comparison [52], [53], [54], [55], or as a source of inspiration for developing newer semantic segmentation architectures [56], [57], [58], [59], [60], [61]; it has also been utilized for multiple applications, such as segmenting objects in biomedical images [62], [63], natural images [64], and satellite images [65], [66]. Recently, Shenoy [67] has independently and systematically investigated UNet++ for the task of "contact prediction model PconsC4", demonstrating significant improvement over widely-used U-Net.…”

Section: Our Previous Workmentioning

confidence: 99%

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

Zhou

Siddiquee

Tajbakhsh

et al. 2020

IEEE Trans. Med. Imaging

2,278

1,038

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The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN). Despite their success, these models have two limitations:(1) their optimal depth is apriori unknown, requiring extensive architecture search or inefficient ensemble of models of varying depths; and (2) their skip connections impose an unnecessarily restrictive fusion scheme, forcing aggregation only at the samescale feature maps of the encoder and decoder sub-networks. To overcome these two limitations, we propose UNet++, a new neural architecture for semantic and instance segmentation, by (1) alleviating the unknown network depth with an efficient ensemble of U-Nets of varying depths, which partially share an encoder and co-learn simultaneously using deep supervision; (2) redesigning skip connections to aggregate features of varying semantic scales at the decoder sub-networks, leading to a highly flexible feature fusion scheme; and (3) devising a pruning scheme to accelerate the inference speed of UNet++. We have evaluated UNet++ using six different medical image segmentation datasets, covering multiple imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and electron microscopy (EM), and demonstrating that (1) UNet++ consistently outperforms the baseline models for the task of semantic segmentation across different datasets and backbone architectures; (2) UNet++ enhances segmentation quality of varying-size objects-an improvement over the fixed-depth U-Net; (3) Mask RCNN++ (Mask R-CNN with UNet++ design) outperforms the original Mask R-CNN for the task of instance segmentation; and (4) pruned UNet++ models achieve significant speedup while showing only modest performance degradation. Our implementation and pre-trained models are available at https://github.com/MrGiovanni/UNetPlusPlus.

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Section: Our Previous Workmentioning

confidence: 99%

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

Zhou

Siddiquee

Tajbakhsh

et al. 2020

IEEE Trans. Med. Imaging

2,278

1,038

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show abstract

“…The motivation behind this was to address the semantic gap between both halves of U-Net prior to concatenation [12]. The work by [20] combined U-Net++ and Half-Dense U-Net [21], which also shares properties of dense networks [22] and standard U-Net [13]. In [20], the combination of both networks was done specifically to target difficulties in combining low-level and top-level features in convolutional neural networks.…”

Section: U-net++mentioning

confidence: 99%

Exploring the U-Net++ Model for Automatic Brain Tumor Segmentation

Micallef¹,

Seychell

Bajada

2021

IEEE Access

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The accessibility and potential of deep learning techniques have increased considerably over the past years. Image segmentation is one of the many fields which have seen novel implementations being developed to solve problems in the domain. U-Net is an example of a popular deep learning model designed specifically for biomedical image segmentation, initially proposed for cell segmentation. We propose a variation of the U-Net++ model, which is itself an adaptation of U-Net, and evaluate its brain tumor segmentation capabilities. The proposed approach obtained Dice Coefficient scores of 0.7192, 0.8712, and 0.7817 for the Enhancing Tumor, Whole Tumor and Tumor Core classes of the BraTS 2019 challenge Validation Dataset. The proposed approach differs from the standard U-Net++ model in a number of ways, including the loss function, number of convolutional blocks, and method of employing deep supervision. Data augmentation and post-processing techniques were also implemented and observed to substantially improve the model predictions. Thus, this article presents a novel adaptation of the U-Net++ architecture, which is both lightweight, and performs comparably with peer-reviewed work evaluated on the same data.

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R2U++: a multiscale recurrent residual U-Net with dense skip connections for medical image segmentation

Mubashar

Ali

Grönlund

et al. 2022

Neural Comput & Applic

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U-Net is a widely adopted neural network in the domain of medical image segmentation. Despite its quick embracement by the medical imaging community, its performance suffers on complicated datasets. The problem can be ascribed to its simple feature extracting blocks: encoder/decoder, and the semantic gap between encoder and decoder. Variants of U-Net (such as R2U-Net) have been proposed to address the problem of simple feature extracting blocks by making the network deeper, but it does not deal with the semantic gap problem. On the other hand, another variant UNET++ deals with the semantic gap problem by introducing dense skip connections but has simple feature extraction blocks. To overcome these issues, we propose a new U-Net based medical image segmentation architecture R2U++. In the proposed architecture, the adapted changes from vanilla U-Net are: (1) the plain convolutional backbone is replaced by a deeper recurrent residual convolution block. The increased field of view with these blocks aids in extracting crucial features for segmentation which is proven by improvement in the overall performance of the network. (2) The semantic gap between encoder and decoder is reduced by dense skip pathways. These pathways accumulate features coming from multiple scales and apply concatenation accordingly. The modified architecture has embedded multi-depth models, and an ensemble of outputs taken from varying depths improves the performance on foreground objects appearing at various scales in the images. The performance of R2U++ is evaluated on four distinct medical imaging modalities: electron microscopy, X-rays, fundus, and computed tomography. The average gain achieved in IoU score is 1.5 ± 0.37% and in dice score is 0.9 ± 0.33% over UNET++, whereas, 4.21 ± 2.72 in IoU and 3.47 ± 1.89 in dice score over R2U-Net across different medical imaging segmentation datasets.

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An Improved Framework Called Du++ Applied to Brain Tumor Segmentation

Cited by 7 publications

References 10 publications

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

Exploring the U-Net++ Model for Automatic Brain Tumor Segmentation

R2U++: a multiscale recurrent residual U-Net with dense skip connections for medical image segmentation

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