Automatic Segmentation Based on Deep Learning Techniques for Diabetic Foot Monitoring Through Multimodal Images

Hernández, Abián; Arteaga-Marrero, Natalia; Villa, Enrique; Fabelo, Himar; Ruiz-Alzola, Juan

doi:10.1007/978-3-030-30645-8_38

Cited by 8 publications

(15 citation statements)

References 14 publications

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“…This approach was previously implemented and reported [ 20 ]. The pixelwise segmentation generated was based on the U-Net architecture, a convolutional neural network, initially proposed for biomedical image segmentation, that consists of a contracting path or encoder and an expanding path or decoder [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

“…The results presented were generated using the model reported earlier [ 20 ]. The neural network was not trained with the newly generated dataset (registered RGB-D-IR), instead the training dataset consisted of 30 original (registered RGB-D) and 17 augmented images.…”

Section: Methodsmentioning

confidence: 99%

“…This unsupervised approach consisted in a pixel-based technique for human skin detection [ 30 ] complemented with the RANSAC estimator described above [ 20 , 29 ]. Shortly, skin pixels were recognised by a threshold based on three color models which are RGB, HSV (Hue, Saturation, Value) and YCbCr (Luminance, Chrominance).…”

Section: Methodsmentioning

confidence: 99%

“…Multimodal imaging facilitates the segmentation process [ 20 ] since structural and functional information of the tissues can be acquired [ 21 ]. Visual-light images (RGB: Red, Green and Blue color space) provide structural information, primarily a clear feet delineation required to establish the ground truth.…”

Section: Introductionmentioning

confidence: 99%

“…In the present work, the focus has been placed on the segmentation procedure, and since the detection of anomalies have not been contemplated yet, only healthy subjects have been considered for the newly created database. Recently, continuing with the non-constrained acquisition protocol, we explored the feasibility of a unified RGB-based Deep Learning approach with point cloud processing, derived from the spatial information provided by the depth images (D), to improve the robustness of the semantic segmentation [ 20 ]. This workflow favours the benefits of the transfer-learning technique where layers are initialised using layers from other networks trained with different RGB image databases, mainly ImageNet.…”

Section: Introductionmentioning

confidence: 99%

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Segmentation Approaches for Diabetic Foot Disorders

Arteaga-Marrero

Hernández

Villa

et al. 2021

Sensors

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Thermography enables non-invasive, accessible, and easily repeated foot temperature measurements for diabetic patients, promoting early detection and regular monitoring protocols, that limit the incidence of disabling conditions associated with diabetic foot disorders. The establishment of this application into standard diabetic care protocols requires to overcome technical issues, particularly the foot sole segmentation. In this work we implemented and evaluated several segmentation approaches which include conventional and Deep Learning methods. Multimodal images, constituted by registered visual-light, infrared and depth images, were acquired for 37 healthy subjects. The segmentation methods explored were based on both visual-light as well as infrared images, and optimization was achieved using the spatial information provided by the depth images. Furthermore, a ground truth was established from the manual segmentation performed by two independent researchers. Overall, the performance level of all the implemented approaches was satisfactory. Although the best performance, in terms of spatial overlap, accuracy, and precision, was found for the Skin and U-Net approaches optimized by the spatial information. However, the robustness of the U-Net approach is preferred.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Segmentation Approaches for Diabetic Foot Disorders

Arteaga-Marrero

Hernández

Villa

et al. 2021

Sensors

Self Cite

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Performance Evaluation of Deep Learning Models for Image Classification Over Small Datasets: Diabetic Foot Case Study

et al. 2022

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Data scarcity is a common and challenging issue when working with Artificial Intelligence solutions, especially those including Deep Learning (DL) models for tasks such as image classification. This is particularly relevant in healthcare scenarios, in which data collection requires a long-lasting process, involving specific control protocols. The performance of DL models is usually quantified by different classification metrics, which may provide biased results, due to the lack of sufficient data. In this paper, an innovative approach is proposed to evaluate the performance of DL models when labeled data is scarce. This approach, which aims to detect the poor performance provided by DL models, in spite of traditional assessing metrics indicating otherwise, is based on information theoretic concepts and motivated by the Information Bottleneck framework. This methodology has been evaluated by implementing several experimental configurations to classify samples from a plantar thermogram dataset, focused on early stage detection of diabetic foot ulcers, as a case study. The proposed network architectures exhibited high results in terms of classification metrics. However, as our approach shows, only two of those models are indeed consistent to generalize the data properly. In conclusion, a new methodology was introduced and tested to identify promising DL models for image classification over small datasets without relying exclusively on the widely employed classification metrics.

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