Simultaneous wound border segmentation and tissue classification using a conditional generative adversarial network

Sarp, Salih; Kuzlu, Murat; Pipattanasomporn, Manisa; Güler, Özgür

doi:10.1049/tje2.12016

Cited by 19 publications

(14 citation statements)

References 30 publications

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“…However, segmentation techniques, and more specifically semantic segmentation methods, are essential for developing a wound size measurement algorithm. There are several image-based wound periphery measurement methods which utilize deep and non-deep learning-based wound image segmentation approaches [19][20][21][22][23][24][25][26]. Deep learning-based techniques have the potential to be significantly more accurate but require larger datasets [27].…”

Section: Introductionmentioning

confidence: 99%

Automatic wound detection and size estimation using deep learning algorithms

et al. 2022

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Evaluating and tracking wound size is a fundamental metric for the wound assessment process. Good location and size estimates can enable proper diagnosis and effective treatment. Traditionally, laboratory wound healing studies include a collection of images at uniform time intervals exhibiting the wounded area and the healing process in the test animal, often a mouse. These images are then manually observed to determine key metrics —such as wound size progress— relevant to the study. However, this task is a time-consuming and laborious process. In addition, defining the wound edge could be subjective and can vary from one individual to another even among experts. Furthermore, as our understanding of the healing process grows, so does our need to efficiently and accurately track these key factors for high throughput (e.g., over large-scale and long-term experiments). Thus, in this study, we develop a deep learning-based image analysis pipeline that aims to intake non-uniform wound images and extract relevant information such as the location of interest, wound only image crops, and wound periphery size over-time metrics. In particular, our work focuses on images of wounded laboratory mice that are used widely for translationally relevant wound studies and leverages a commonly used ring-shaped splint present in most images to predict wound size. We apply the method to a dataset that was never meant to be quantified and, thus, presents many visual challenges. Additionally, the data set was not meant for training deep learning models and so is relatively small in size with only 256 images. We compare results to that of expert measurements and demonstrate preservation of information relevant to predicting wound closure despite variability from machine-to-expert and even expert-to-expert. The proposed system resulted in high fidelity results on unseen data with minimal human intervention. Furthermore, the pipeline estimates acceptable wound sizes when less than 50% of the images are missing reference objects.

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Section: Introductionmentioning

confidence: 99%

Automatic wound detection and size estimation using deep learning algorithms

et al. 2022

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“…D_real and D_fake loss functions for real and fake samples directly update the discriminator network. Generator network weights are also updated with two loss functions, adversarial loss (G_GAN) from discriminator network and L1 loss (G_L1) [37]. The training of discriminator and generator networks is a zerosum game and causes a non-converging problem [38].…”

Section: Results Of Cgan-based Modelmentioning

confidence: 99%

A comparison of deep learning algorithms on image data for detecting floodwater on roadways

et al. 2022

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Object detection and segmentation algorithms evolved significantly in the last decade. Simultaneous object detection and segmentation paved the way for real-time applications such as autonomous driving. Detection and segmentation of (partially) flooded roadways are essential inputs for vehicle routing and traffic management systems. This paper proposes an automatic floodwater detection and segmentation method utilizing the Mask Region-Based Convolutional Neural Networks (Mask-R-CNN) and Generative Adversarial Networks (GAN) algorithms. To train the model, manually labeled images with urban, suburban, and natural settings are used. The performances of the algorithms are assessed in accurately detecting the floodwater captured in images. The results show that the proposed Mask-R-CNN-based floodwater detection and segmentation outperform previous studies, whereas the GAN-based model has a straightforward implementation compared to other models.

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“…The ROC curve and the AUC provide a visualization related to the performance of the model on the classification task. The performance of the model could be improved with a larger training dataset [45] and fine-tuning the hyperparameters [46].…”

Section: Resultsmentioning

confidence: 99%

The Enlightening Role of Explainable Artificial Intelligence in Chronic Wound Classification

Sarp

Kuzlu

Wilson³

et al. 2021

Electronics

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Artificial Intelligence (AI) has been among the most emerging research and industrial application fields, especially in the healthcare domain, but operated as a black-box model with a limited understanding of its inner working over the past decades. AI algorithms are, in large part, built on weights calculated as a result of large matrix multiplications. It is typically hard to interpret and debug the computationally intensive processes. Explainable Artificial Intelligence (XAI) aims to solve black-box and hard-to-debug approaches through the use of various techniques and tools. In this study, XAI techniques are applied to chronic wound classification. The proposed model classifies chronic wounds through the use of transfer learning and fully connected layers. Classified chronic wound images serve as input to the XAI model for an explanation. Interpretable results can help shed new perspectives to clinicians during the diagnostic phase. The proposed method successfully provides chronic wound classification and its associated explanation to extract additional knowledge that can also be interpreted by non-data-science experts, such as medical scientists and physicians. This hybrid approach is shown to aid with the interpretation and understanding of AI decision-making processes.

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Simultaneous wound border segmentation and tissue classification using a conditional generative adversarial network

Cited by 19 publications

References 30 publications

Automatic wound detection and size estimation using deep learning algorithms

Automatic wound detection and size estimation using deep learning algorithms

A comparison of deep learning algorithms on image data for detecting floodwater on roadways

The Enlightening Role of Explainable Artificial Intelligence in Chronic Wound Classification

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