Tensor Decomposition for Colour Image Segmentation of Burn Wounds

Cirillo, Marco Domenico; Mirdell, Robin; Sjöberg, Folke; Pham, Tuan D.

doi:10.1038/s41598-019-39782-2

Cited by 25 publications

(13 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…No automation and quantification allowing diagnosis are proposed. One rare quantitative approach using Matlab computing language is tensor decomposition for color image segmentation of burn wounds [20]. The aim is to use a new automated segmentation of the images in order to determinate the burn area.…”

Section: Discussionmentioning

confidence: 99%

Software development for autologous skin substitute production

Mestrallet

2021

Preprint

View full text Add to dashboard Cite

The gold-standard for the management of patients affected by large-surface third-degree burns is autologous skin graft. When burns affect <40% total body surface area (TBSA), meshed skin samples harvested from non-affected donor sites can be used as grafts. In more severe cases corresponding to burns affecting >40% TBSA), the donor site surfaces are insufficient. The alternative grafting strategy uses bioengineered skin substitutes that are generated using the own keratinocytes of the patient after ex vivo expansion. Optimal setup of the bioengineering process involved determination of the required graft surface, adjustment of cell quantities, and control of the timing necessary for production. Accordingly, tools to assist the design of personalized protocols will certainly contribute to care quality and cost limitation. Here, I describe the principle of a software-assisted calculation of the required graft surface and keratinocyte numbers needed, according to specific patient clinical characteristics. The software also offers assistance to estimate the Baux score, a method that has been proposed to link the severity of burn injuries and the prognosis for the patient. In conclusion, this software provides a principle of assisted burned patient diagnose and skin substitute bioengineering process which development may facilitate the design of personalized protocols for skin regenerative cell therapies.

show abstract

Section: Discussionmentioning

confidence: 99%

Software development for autologous skin substitute production

Mestrallet

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Fuzzy-ArtMap or Multidimensional Scaling Analyses were the methods used. The second generation, a cluster of four studies published in 2019 and 2020 from four countries, takes advantage of increased computational power and uses more advanced algorithm techniques (e.g., Support Vector Machine) [18,[27][28][29]. This generation still requires that the images be pre-processed to feed the algorithm with image-specific features rather than images themselves.…”

Section: Peer-reviewed Scientific Articles Over Time and By Locationmentioning

confidence: 99%

Accuracy of Image-Based Automated Diagnosis in the Identification and Classification of Acute Burn Injuries. A Systematic Review

Boissin

Laflamme

2021

EBJ

View full text Add to dashboard Cite

Although they are a common type of injury worldwide, burns are challenging to diagnose, not least by untrained point-of-care clinicians. Given their visual nature, developments in artificial intelligence (AI) have sparked growing interest in the automated diagnosis of burns. This review aims to appraise the state of evidence thus far, with a focus on the identification and severity classification of acute burns. Three publicly available electronic databases were searched to identify peer-reviewed studies on the automated diagnosis of acute burns, published in English since 2005. From the 20 identified, three were excluded on the grounds that they concerned animals, older burns or lacked peer review. The remaining 17 studies, from nine different countries, were classified into three AI generations, considering the type of algorithms developed and the images used. Whereas the algorithms for burn identification have not gained much in accuracy across generations, those for severity classification improved substantially (from 66.2% to 96.4%), not least in the latest generation (n = 8). Those eight studies were further assessed for methodological bias and results applicability, using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. This highlighted the feasibility nature of the studies and their detrimental dependence on online databases of poorly documented images, at the expense of a substantial risk for patient selection and limited applicability in the clinical setting. In moving past the pilot stage, future development work would benefit from greater input from clinicians, who could contribute essential point-of-care knowledge and perspectives.

show abstract

“…Tensor decomposition 20,21 has been increasingly applied for solving challenging problems in medicine and health over recent years [22][23][24][25][26][27][28] . In cancer, tensor decomposition has recently been applied for identifying microRNA (miRNA) and mRNA expression profiles as potential prognostic biomarkers for kidney renal clear cell carcinoma, where genes involving in cancerrelated pathways were found to be associated with miRNAs 29 .…”

Section: Introductionmentioning

confidence: 99%

Tensor Decomposition of Largest Convolutional Eigenvalues Reveals Pathological Predictive Power of RhoB in Rectal Cancer Biopsy

Pham

Ravi

Liu

et al. 2021

Preprint

View full text Add to dashboard Cite

RhoB protein belongs to the Rho GTPase family, which plays an important role in governing cell signaling and tissue morphology. RhoB expression is known to have implications in pathological processes of diseases. Investigation in the regulation and communication of this protein detected by immunohistochemical staining on the microscope is worth exploring to gain insightful information that may lead to identifying optimal disease treatment options. In particular, the role of RhoB in rectal cancer is not well-discovered. Here, we report that methods of deep learning-based image analysis and the decomposition of multiway arrays discover the predictive factor of RhoB in two cohorts of rectal-cancer patients having survival rates of less and more than 5 years. The analysis results show distinctions between the tensor-decomposition factors of the two cohorts.

show abstract

Tensor Decomposition for Colour Image Segmentation of Burn Wounds

Cited by 25 publications

References 37 publications

Software development for autologous skin substitute production

Software development for autologous skin substitute production

Accuracy of Image-Based Automated Diagnosis in the Identification and Classification of Acute Burn Injuries. A Systematic Review

Tensor Decomposition of Largest Convolutional Eigenvalues Reveals Pathological Predictive Power of RhoB in Rectal Cancer Biopsy

Contact Info

Product

Resources

About