Tissue classification and segmentation of pressure injuries using convolutional neural networks

Zahia, Sofia; Sierra-Sosa, Daniel; Garcia‐Zapirain, Begonya; Elmaghraby, Adel

doi:10.1016/j.cmpb.2018.02.018

Cited by 68 publications

(50 citation statements)

References 17 publications

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“…Region segmentation. Regions of interest within images are detached either by parameter-specific methods, e.g., FCN-Net [29] and SegNet [27], or generic methods, e.g., uniform grids [5]. Superpixels are an alternative to both parameter-specific and generic methods, as they split an image I into k p regions with well-defined borders [12].…”

Section: Preliminariesmentioning

confidence: 99%

“…Three fully-connected layers generate patch representations to be further labeled by an SVM. The strategy of Zahia et al [5] relies on a similar approach but uses a CNN with nine layers for the labeling of grid regions. The main drawback with those approaches is they are tightly coupled to specific DL models so that the strategies may not benefit from isolated enhancements on segmentation, feature extraction, or classification.…”

Section: Preliminariesmentioning

confidence: 99%

“…The authors addressed such imbalance by using an approximation of the NP-hard problem that divides the instances into training and test sets. Likewise, Zahia et al [5] investigated 22 pressure ulcer images, which were divided into 270,762 regions of granulation, 80,636 parts of slough, and 37,146 of necrosis. The authors used weighted classification metrics for reporting the results.…”

Section: Preliminariesmentioning

confidence: 99%

“…can be automatically evaluated by Computer-Aided Diagnosis (CAD) tools, or even used for the searching of massive databases through content-only queries, as in Content-Based Image Retrieval (CBIR) applications. In both CAD and CBIR cases, the detection of abnormalities requires the extraction of patterns from images, while a decision-making strategy is necessary for juxtaposing new images to those in the database [4,5].Since dermatological lesions are routinely diagnosed by biopsies and surrounding skin aspects, ulcers can be computationally characterized by particular types of tissues (and their areas) within the wounded region [6,7]. For instance, Mukherjee et al[8] proposed a five-color classification model and applied a color-based low-level extractor further labeled by a Support-Vector Machine (SVM) strategy at an 87.61% hit ratio.…”

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confidence: 99%

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A superpixel-driven deep learning approach for the analysis of dermatological wounds

Blanco¹,

Traina²,

Traina³

et al. 2020

Computer Methods and Programs in Biomedicine

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Background. The image-based identification of distinct tissues within dermatological wounds enhances patients' care since it requires no intrusive evaluations. This manuscript presents an approach, we named QTDU , that combines deep learning models with superpixel-driven segmentation methods for assessing the quality of tissues from dermatological ulcers. Method. QTDU consists of a three-stage pipeline for the obtaining of ulcer segmentation, tissues' labeling, and wounded area quantification. We set up our approach by using a real and annotated set of dermatological ulcers for training several deep learning models to the identification of ulcered superpixels. Results. Empirical evaluations on 179,572 superpixels divided into four classes showed QTDU accurately spot wounded tissues (AUC = 0.986, sensitivity = 0.97, and specificity = 0.974) and outperformed machinelearning approaches in up to 8.2% regarding F1-Score through fine-tuning of a ResNet-based model. Last, but not least, experimental evaluations also showed QTDU correctly quantified wounded tissue areas within a 0.089 Mean Absolute Error ratio. Conclusions. Results indicate QTDU effectiveness for both tissue segmentation and wounded area quantification tasks. When compared to existing machine-learning approaches, the combination of superpixels and deep learning models outperformed the competitors within strong significant levels. can be automatically evaluated by Computer-Aided Diagnosis (CAD) tools, or even used for the searching of massive databases through content-only queries, as in Content-Based Image Retrieval (CBIR) applications. In both CAD and CBIR cases, the detection of abnormalities requires the extraction of patterns from images, while a decision-making strategy is necessary for juxtaposing new images to those in the database [4,5].Since dermatological lesions are routinely diagnosed by biopsies and surrounding skin aspects, ulcers can be computationally characterized by particular types of tissues (and their areas) within the wounded region [6,7]. For instance, Mukherjee et al.[8] proposed a five-color classification model and applied a color-based low-level extractor further labeled by a Support-Vector Machine (SVM) strategy at an 87.61% hit ratio. Such idea of concatenating feature extraction and classification is found at the core of most wound segmentation strategies, as in the study of Kavitha et al.[9] that evaluated leg ulcerations by extracting patterns based on local spectral histograms to be labeled by a Multi-Layer Perceptron (MLP) classifier with 87.05% accuracy. Analogously, Pereyra et al.[10] discussed the use of color descriptors and an Instancebased Learning (IbL) classifier with a 61.7% hit ratio, whereas Veredas et al. [11] suggested the use of texture descriptors and an MLP classifier with 84.84% accuracy.Blanco et al.[4] and Chino et al.[12] followed a slightly different premise for finding proper similarity measures and comparison criteria for dermatological wounds. Their approaches are based on a divide-andconquer stra...

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Section: Preliminariesmentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

mentioning

confidence: 99%

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A superpixel-driven deep learning approach for the analysis of dermatological wounds

Blanco¹,

Traina²,

Traina³

et al. 2020

Computer Methods and Programs in Biomedicine

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“…Classical CNN framework consists of three main building blocks: fully connected layers, pooling layers, and convolutional layers. Some recent studies have claimed that it is also suitable for MRI segmentation and brain tissue detection [22,23]. However, for the classical CNN, it is a tedious task to recognize image patches.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Brain MR Images by Using Fully Convolutional Network and Gaussian Mixture Model with Spatial Constraints

Lai

Zhu

Ling

2019

Mathematical Problems in Engineering

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Accurate segmentation of brain tissue from magnetic resonance images (MRIs) is a critical task for diagnosis, treatment, and clinical research. In this paper, a novel algorithm (GMMD-U) that incorporates the modified full convolutional neural network U-net and Gaussian-Dirichlet mixture model (GMMD) with spatial constraints is presented. The proposed GMMD-U considers the local spatial relationships by assuming that the prior probability obeys the Dirichlet distribution. Specifically, GMMD is applied for extracting brain tissue that has a distinct intensity region and modified U-net is exploited to correct the wrong-classification areas caused by GMMD or other conventional approaches. The proposed GMMD-U is designed to take advantage of the statistical model-based segmentation techniques and deep neural network. We evaluate the performance of GMMD-U on a publicly available brain MRI dataset by comparing it with several existing algorithms, and the results reported reveal that the proposed framework can accurately detect the brain tissue from MRIs. The proposed learning-based integrated framework could be effective for brain tissue segmentation, which will be helpful for surgeons in brain disease diagnosis.

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A Superpixel-Wise Fully Convolutional Neural Network Approach for Diabetic Foot Ulcer Tissue Classification

Niri¹,

Douzi²,

Lucas³

et al. 2021

Lecture Notes in Computer Science

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Tissue classification and segmentation of pressure injuries using convolutional neural networks

Cited by 68 publications

References 17 publications

A superpixel-driven deep learning approach for the analysis of dermatological wounds

A superpixel-driven deep learning approach for the analysis of dermatological wounds

Segmentation of Brain MR Images by Using Fully Convolutional Network and Gaussian Mixture Model with Spatial Constraints

A Superpixel-Wise Fully Convolutional Neural Network Approach for Diabetic Foot Ulcer Tissue Classification

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