Segmentation and Measurement of Chronic Wounds for Bioprinting

Gholami, Peyman; Ahmadi-Pajouh, Mohammad Ali; Abolftahi, Nabiollah; Hamarneh, Ghassan; Kayvanrad, Mohammad H.

doi:10.1109/jbhi.2017.2743526

Cited by 29 publications

(20 citation statements)

References 46 publications

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“…Traditionally, wound image segmentation was performed using image processing techniques [ 3 ]. Several image processing approaches have been proposed in the literature, such as Region-based segmentation [ 14 , 15 , 16 , 17 ], and edge-based segmentation [ 14 , 18 , 19 ]. In addition, traditional machine learning methods were used to perform wound segmentation.…”

Section: Related Workmentioning

confidence: 99%

Integrating 3D Model Representation for an Accurate Non-Invasive Assessment of Pressure Injuries with Deep Learning

Zahia

Garcia‐Zapirain

Elmaghraby

2020

Sensors

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Pressure injuries represent a major concern in many nations. These wounds result from prolonged pressure on the skin, which mainly occur among elderly and disabled patients. If retrieving quantitative information using invasive methods is the most used method, it causes significant pain and discomfort to the patients and may also increase the risk of infections. Hence, developing non-intrusive methods for the assessment of pressure injuries would represent a highly useful tool for caregivers and a relief for patients. Traditional methods rely on findings retrieved solely from 2D images. Thus, bypassing the 3D information deriving from the deep and irregular shape of this type of wounds leads to biased measurements. In this paper, we propose an end-to-end system which uses a single 2D image and a 3D mesh of the pressure injury, acquired using the Structure Sensor, and outputs all the necessary findings such as: external segmentation of the wound as well as its real-world measurements (depth, area, volume, major axis and minor axis). More specifically, a first block composed of a Mask RCNN model uses the 2D image to output the segmentation of the external boundaries of the wound. Then, a second block matches the 2D and 3D views to segment the wound in the 3D mesh using the segmentation output and generates the aforementioned real-world measurements. Experimental results showed that the proposed framework can not only output refined segmentation with 87% precision, but also retrieves reliable measurements, which can be used for medical assessment and healing evaluation of pressure injuries.

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Section: Related Workmentioning

confidence: 99%

Integrating 3D Model Representation for an Accurate Non-Invasive Assessment of Pressure Injuries with Deep Learning

Zahia

Garcia‐Zapirain

Elmaghraby

2020

Sensors

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“…This study [27] utilises traditional pre‐ and post‐processing steps to improve segmentation performance and does not have tissue classification. The authors in [28] provide a tool for segmenting and locating chronic wounds to facilitate bioprinting treatment using edge detection and segmentation algorithms. In [28], the authors utilise semi‐automatic overall wound segmentation on a limited number of wound images.…”

Section: Introductionmentioning

confidence: 99%

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

Sarp

Kuzlu

Pipattanasomporn

et al. 2021

J. eng.

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Generative adversarial network (GAN) applications on medical image synthesis have the potential to assist caregivers in deciding a proper chronic wound treatment plan by understanding the border segmentation and the wound tissue classification visually. This study proposes a hybrid wound border segmentation and tissue classification method utilising conditional GAN, which can mimic real data without expert knowledge. We trained the network on chronic wound datasets with different sizes. The performance of the GAN algorithm is evaluated through the mean squared error, Dice coefficient metrics and visual inspection of generated images. This study also analyses the optimum number of training images as well as the number of epochs using GAN for wound border segmentation and tissue classification. The results show that the proposed GAN model performs efficiently for wound border segmentation and tissue classification tasks with a set of 2000 images at 200 epochs.

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“…Many of the existing researches on wound segmentation are based on feature engineering [5], [6], [9]- [11], which we call traditional methods here. Compared to deep learning, these methods require human-designed features to segment images.…”

Section: Introductionmentioning

confidence: 99%

Wound Segmentation Network Based on Location Information Enhancement

Wang

Peng

et al. 2019

IEEE Access

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Wound segmentation provides assistance for the diagnosis and treatment of wounds. We find that the wound image has a distinct feature, e.g., the pixel color changes gradually according to its position. Location information is essential to describe this feature. However, the current methods of wound segmentation based on deep learning have not significantly added location information into model training. In order to enhance this information, we propose a deep neural network model based on a location map and location-enhanced convolution kernel. The model effectively encodes the location information to one feature map, which is then concatenated with the inputs of the network and added to the hidden layer of the network after downsampling. Moreover, the model uses a fixed-value initialized convolution kernel to further enhance the location information in the training of the network. At the end of the model, a fixed-value depth-wise convolution layer is added to eliminate minor errors.

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Segmentation and Measurement of Chronic Wounds for Bioprinting

Abstract: this study is the first to perform wound bioprinting based on image segmentation. It also compares several segmentation methods used for this purpose to determine the best.

Cited by 29 publications

References 46 publications

Integrating 3D Model Representation for an Accurate Non-Invasive Assessment of Pressure Injuries with Deep Learning

Integrating 3D Model Representation for an Accurate Non-Invasive Assessment of Pressure Injuries with Deep Learning

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

Wound Segmentation Network Based on Location Information Enhancement

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