Adaptive Pyramid Context Network for Semantic Segmentation

He, Junjun; Deng, Zhongying; Zhou, Lei; Wang, Yali; Qiao, Yu

doi:10.1109/cvpr.2019.00770

Cited by 340 publications

(189 citation statements)

References 26 publications

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“…To target these issues we aimed to improve spatial information integration using multiscale- and 3-D variants. Multiscale architectures, i.e., fusion of information from multiple resolutions—were shown to be beneficial in boundary detection and segmentation problems (Kokkinos, 2015 ; Havaei et al, 2016 ; Kamnitsas et al, 2017 ; Stawiaski, 2017 ; He et al, 2019 ; Sinha and Jose, 2020 ). Some recent works proposed re-thinking of skip-connections within the Unet architecture to effectively match semantics and fuse information on multiple resolutions for prediction (Huang et al, 2020 ; Zhou et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

“…Second, it was beyond the scope of this work to integrate all potentially beneficial architecture modifications. Further future modifications could utilize densely connected convolutional blocks to enhance feature propagation through encoding levels (Zhang et al, 2018 ) or even a more complex Adaptive Pyramid Context Network with local affinity integration to advance multiscale context aggregation using global representation (He et al, 2019 ). Moreover, the Unet++ architecture shown in Zhou et al ( 2020 ) would seem like a well fit candidate for direct comparison.…”

Section: Discussionmentioning

confidence: 99%

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BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation in Patients With Cerebrovascular Disease

Hilbert

Madai

Akay

et al. 2020

Front. Artif. Intell.

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Introduction: Arterial brain vessel assessment is crucial for the diagnostic process in patients with cerebrovascular disease. Non-invasive neuroimaging techniques, such as time-of-flight (TOF) magnetic resonance angiography (MRA) imaging are applied in the clinical routine to depict arteries. They are, however, only visually assessed. Fully automated vessel segmentation integrated into the clinical routine could facilitate the time-critical diagnosis of vessel abnormalities and might facilitate the identification of valuable biomarkers for cerebrovascular events. In the present work, we developed and validated a new deep learning model for vessel segmentation, coined BRAVE-NET, on a large aggregated dataset of patients with cerebrovascular diseases. Methods: BRAVE-NET is a multiscale 3-D convolutional neural network (CNN) model developed on a dataset of 264 patients from three different studies enrolling patients with cerebrovascular diseases. A context path, dually capturing high-and low-resolution volumes, and deep supervision were implemented. The BRAVE-NET model was compared to a baseline Unet model and variants with only context paths and deep supervision, respectively. The models were developed and validated using high-quality manual labels as ground truth. Next to precision and recall, the performance was assessed quantitatively by Dice coefficient (DSC); average Hausdorff distance (AVD); 95-percentile Hausdorff distance (95HD); and via visual qualitative rating. Results: The BRAVE-NET performance surpassed the other models for arterial brain vessel segmentation with a DSC = 0.931, AVD = 0.165, and 95HD = 29.153. The BRAVE-NET model was also the most resistant toward false labelings as revealed by the visual analysis. The performance improvement is primarily attributed to the integration Hilbert et al. Fully-Automated Arterial Brain Vessel Segmentation of the multiscaling context path into the 3-D Unet and to a lesser extent to the deep supervision architectural component. Discussion: We present a new state-of-the-art of arterial brain vessel segmentation tailored to cerebrovascular pathology. We provide an extensive experimental validation of the model using a large aggregated dataset encompassing a large variability of cerebrovascular disease and an external set of healthy volunteers. The framework provides the technological foundation for improving the clinical workflow and can serve as a biomarker extraction tool in cerebrovascular diseases.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation in Patients With Cerebrovascular Disease

Hilbert

Madai

Akay

et al. 2020

Front. Artif. Intell.

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“…Zhang et al [82] encode semantic context to network and stress class-dependent feature maps. He et al [30] propose adaptively pyramid context module to capture global-guided local affinity. Fu et al [21] integrate local and global dependencies with both spatial and channel attention.…”

Section: Related Work 21 Scene Segmentationmentioning

confidence: 99%

Boundary-Aware Feature Propagation for Scene Segmentation

Ding

Jiang

Liu

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

228

107

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In this work, we address the challenging issue of scene segmentation. To increase the feature similarity of the same object while keeping the feature discrimination of different objects, we explore to propagate information throughout the image under the control of objects' boundaries. To this end, we first propose to learn the boundary as an additional semantic class to enable the network to be aware of the boundary layout. Then, we propose unidirectional acyclic graphs (UAGs) to model the function of undirected cyclic graphs (UCGs), which structurize the image via building graphic pixel-by-pixel connections, in an efficient and effective way. Furthermore, we propose a boundaryaware feature propagation (BFP) module to harvest and propagate the local features within their regions isolated by the learned boundaries in the UAG-structured image. The proposed BFP is capable of splitting the feature propagation into a set of semantic groups via building strong connections among the same segment region but weak connections between different segment regions. Without bells and whistles, our approach achieves new state-of-the-art segmentation performance on three challenging semantic segmentation datasets, i.e., PASCAL-Context, CamVid, and Cityscapes.

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“…However, dilated convolution has limitations. The input feature map is in a square window, and this will increase the receptive field while absorbing some irrelevant information from irrelevant regions [ 32 , 33 ] for some features with long-distance edges (lanes, slot markers, etc.). The proposed method makes use of dilated convolutions with a strip shape.…”

Section: Related Workmentioning

confidence: 99%

UnetDVH-Linear: Linear Feature Segmentation by Dilated Convolution with Vertical and Horizontal Kernels

Liao

Cao

et al. 2020

Sensors

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Linear feature extraction is crucial for special objects in semantic segmentation networks, such as slot marking and lanes. The objects with linear characteristics have global contextual information dependency. It is very difficult to capture the complete information of these objects in semantic segmentation tasks. To improve the linear feature extraction ability of the semantic segmentation network, we propose introducing the dilated convolution with vertical and horizontal kernels (DVH) into the task of feature extraction in semantic segmentation networks. Meanwhile, we figure out the outcome if we put the different vertical and horizontal kernels on different places in the semantic segmentation networks. Our networks are trained on the basis of the SS dataset, the TuSimple lane dataset and the Massachusetts Roads dataset. These datasets consist of slot marking, lanes, and road images. The research results show that our method improves the accuracy of the slot marking segmentation of the SS dataset by 2%. Compared with other state-of-the-art methods, our UnetDVH-Linear (v1) obtains better accuracy on the TuSimple Benchmark Lane Detection Challenge with a value of 97.53%. To prove the generalization of our models, road segmentation experiments were performed on aerial images. Without data argumentation, the segmentation accuracy of our model on the Massachusetts roads dataset is 95.3%. Moreover, our models perform better than other models when training with the same loss function and experimental settings. The experiment result shows that the dilated convolution with vertical and horizontal kernels will enhance the neural network on linear feature extraction.

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Adaptive Pyramid Context Network for Semantic Segmentation

Cited by 340 publications

References 26 publications

BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation in Patients With Cerebrovascular Disease

BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation in Patients With Cerebrovascular Disease

Boundary-Aware Feature Propagation for Scene Segmentation

UnetDVH-Linear: Linear Feature Segmentation by Dilated Convolution with Vertical and Horizontal Kernels

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