Dynamic spectral residual superpixels

Zhang, Jianchao; Avilés-Rivero, Angelica I.; Heydecker, Daniel; Zhuang, Xiaosheng; Chan, Raymond H.; Schönlieb, Carola‐Bibiane

doi:10.1016/j.patcog.2020.107705

Cited by 7 publications

(3 citation statements)

References 61 publications

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“…These versions all demand a higher level of computer calculation. We have adopted the original SLIC to draw boundaries for tissues, because it has three crucial properties: it is fast and simple [ 40 ]; it uses a regular shape and similar size; it delivers good adhesion to the object border.…”

Section: Discussionmentioning

confidence: 99%

Deep learning approach based on superpixel segmentation assisted labeling for automatic pressure ulcer diagnosis

et al. 2022

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A pressure ulcer is an injury of the skin and underlying tissues adjacent to a bony eminence. Patients who suffer from this disease may have difficulty accessing medical care. Recently, the COVID-19 pandemic has exacerbated this situation. Automatic diagnosis based on machine learning (ML) brings promising solutions. Traditional ML requires complicated preprocessing steps for feature extraction. Its clinical applications are thus limited to particular datasets. Deep learning (DL), which extracts features from convolution layers, can embrace larger datasets that might be deliberately excluded in traditional algorithms. However, DL requires large sets of domain specific labeled data for training. Labeling various tissues of pressure ulcers is a challenge even for experienced plastic surgeons. We propose a superpixel-assisted, region-based method of labeling images for tissue classification. The boundary-based method is applied to create a dataset for wound and re-epithelialization (re-ep) segmentation. Five popular DL models (U-Net, DeeplabV3, PsPNet, FPN, and Mask R-CNN) with encoder (ResNet-101) were trained on the two datasets. A total of 2836 images of pressure ulcers were labeled for tissue classification, while 2893 images were labeled for wound and re-ep segmentation. All five models had satisfactory results. DeeplabV3 had the best performance on both tasks with a precision of 0.9915, recall of 0.9915 and accuracy of 0.9957 on the tissue classification; and a precision of 0.9888, recall of 0.9887 and accuracy of 0.9925 on the wound and re-ep segmentation task. Combining segmentation results with clinical data, our algorithm can detect the signs of wound healing, monitor the progress of healing, estimate the wound size, and suggest the need for surgical debridement.

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Section: Discussionmentioning

confidence: 99%

Deep learning approach based on superpixel segmentation assisted labeling for automatic pressure ulcer diagnosis

et al. 2022

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“…Classic Techniques for Superpixels. Since the pioneering work of [28], several techniques have been proposed which can be roughly divided into patch-based models [7,33], watershed techniques [9,4,22], clustering-based approaches [1,17,21,2,23,44] and graph-based techniques [28,6,19,12]. The last two categories are the most widely applied family of techniques, which we will cover in the rest of this section.…”

Section: Related Workmentioning

confidence: 99%

HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation

Peng¹,

Avilés-Rivero²,

Schoenlieb

2022

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

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Superpixels serve as a powerful preprocessing tool in many computer vision tasks. By using superpixel representation, the number of image primitives can be largely reduced by orders of magnitudes. The majority of superpixel methods use handcrafted features, which usually do not translate well into strong adherence to object boundaries. A few recent superpixel methods have introduced deep learning into the superpixel segmentation process. However, none of these methods is able to produce superpixels in near real-time, which is crucial to the applicability of a superpixel method in practice. In this work, we propose a two-stage graph-based framework for superpixel segmentation. In the first stage, we introduce an efficient Deep Affinity Learning (DAL) network that learns pairwise pixel affinities by aggregating multi-scale information. In the second stage, we propose a highly efficient superpixel method called Hierarchical Entropy Rate Segmentation (HERS). Using the learned affinities from the first stage, HERS builds a hierarchical tree structure that can produce any number of highly adaptive superpixels instantaneously. We demonstrate, through visual and numerical experiments, the effectiveness and efficiency of our method compared to various state-of-the-art superpixel methods.

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“…Although using saliency in segmentation is not a novel strategy [12], it has not been thoroughly exploited for generating superpixels until recently. In [32], the authors proposed a SLIC-based algorithm that uses a saliency map based on the Fourier Transform for generating more superpixels in textured regions. The Object-based DISF (ODISF) [3] method is another example that extends DISF for incorporating object saliency maps.…”

Section: Introductionmentioning

confidence: 99%