Automated Wound Image Segmentation: Transfer Learning from Human to Pet via Active Semi-Supervised Learning

Wound management is a fundamental task in standard clinical practice. Automated solutions already exist for humans, but there is a lack of applications regarding wound management for pets. Precise and efficient wound assessment is helpful to improve diagnosis and to increase the effectiveness of treatment plans for chronic wounds. In this work, we introduced a novel pipeline for the segmentation of pet wound images. Starting from a model pre-trained on human-based wound images, we applied a combination of transfer learning (TL) and active semi-supervised learning (ASSL) to automatically label a large dataset. Additionally, we provided a guideline for future applications of TL+ASSL training strategy on image datasets. We compared the effectiveness of the proposed training strategy, monitoring the performance of an EfficientNet-b3 U-Net model against the lighter solution provided by a MobileNet-v2 U-Net model. We obtained 80% of correctly segmented images after five rounds of ASSL training. The EfficientNet-b3 U-Net model significantly outperformed the MobileNet-v2 one. We proved that the number of available samples is a key factor for the correct usage of ASSL training. The proposed approach is a viable solution to reduce the time required for the generation of a segmentation dataset

Effectiveness of Semi-Supervised Active Learning in Automated Wound Image Segmentation

Appropriate wound management shortens the healing times and reduces the management costs, benefiting the patient in physical terms and potentially reducing the healthcare system’s economic burden. Among the instrumental measurement methods, the image analysis of a wound area is becoming one of the cornerstones of chronic ulcer management. Our study aim is to develop a solid AI method based on a convolutional neural network to segment the wounds efficiently to make the work of the physician more efficient, and subsequently, to lay the foundations for the further development of more in-depth analyses of ulcer characteristics. In this work, we introduce a fully automated model for identifying and segmenting wound areas which can completely automatize the clinical wound severity assessment starting from images acquired from smartphones. This method is based on an active semi-supervised learning training of a convolutional neural network model. In our work, we tested the robustness of our method against a wide range of natural images acquired in different light conditions and image expositions. We collected the images using an ad hoc developed app and saved them in a database which we then used for AI training. We then tested different CNN architectures to develop a balanced model, which we finally validated with a public dataset. We used a dataset of images acquired during clinical practice and built an annotated wound image dataset consisting of 1564 ulcer images from 474 patients. Only a small part of this large amount of data was manually annotated by experts (ground truth). A multi-step, active, semi-supervised training procedure was applied to improve the segmentation performances of the model. The developed training strategy mimics a continuous learning approach and provides a viable alternative for further medical applications. We tested the efficiency of our model against other public datasets, proving its robustness. The efficiency of the transfer learning showed that after less than 50 epochs, the model achieved a stable DSC that was greater than 0.95. The proposed active semi-supervised learning strategy could allow us to obtain an efficient segmentation method, thereby facilitating the work of the clinician by reducing their working times to achieve the measurements. Finally, the robustness of our pipeline confirms its possible usage in clinical practice as a reliable decision support system for clinicians

Stratification performance of multinomial-based approach on simulated patients.

Illustration of the accuracy of our proposed maximum-likelihood approach based on multinomials to assign the simulated patients to the HDMM components. The metrics of accuracy is the Adjusted Rand Index (ARI), which is able to deal with scenarios where the observed number of components was found different from the expected one. ARI equals to one matches perfect agreement. The upper quadrant reports the result for K = 5 simulated components, while the lower quadrant does it for K = 10 components. The variables αsim and αHDMM respectively indicate when the expected components were uniform-like simulated (αsim = 1) or were low-overlapping (αsim = 1/M). Similarly, scenarios with αHDMM = 1 indicate when the HDMM was set to find poorly disjunct components, whereas αHDMM = 1/M caused the HDMM to estimate highly disjunct components. The boxplots in the plot summarizes the performance across any average number of genomic alterations per simulated patient.</p

Supporting materials and methods.

This file contains sections deep diving in the detailed aspects of our work from a methodological standpoint. (PDF)</p

Illustration of the herein proposed approaches to enhance the standard workflow for onco-hematological patients stratification.

Description of the common recent workflow in onco-hematology (in the black box) for the analysis of genomic data used to improve the identification of disease components that can potentially support the progress of novel disease classification systems. On the right, we illustrate the herein presented novel approaches to enhance the statistical characterization of components and to provide a maximum-likelihood inspired alternative to perform patients classification. Our approaches are built upon the outcome of the Hierarchical Dirichlet Mixture Model (HDMM) of multinomials that usually fits the data. Given the HDMM outcome, the components are usually characterized by a single or a few genomic drivers inspired by how the HDMM clustered the genomic alterations and by a priori clinical knowledge. In contrast, our approaches utilize the HDMM outcome to respectively characterize the components either as multinomials, i.e., in line with the HDMM, or as Multivariate Fisher Non Central Hypergeometric (MFNCH) distributions. Each distribution models a different urn problem. The multinomials model drawings with replacement from an urn with multiple marbles with different colors. Instead, the MFNCH distributions we use model drawings without replacement from an urn with one single marble per color and with each marble with a different size.</p

Difficulty for HDMM to estimate the expected number of components on simulated patients.

Results on the capability of convergence of the HDMM on simulated data that aim to reproduce the onco-hematological data commonly used to discover novel disease classes. In this plot we observe the number of components estimated by the HDMM (y-axis) along the average number of genomic alterations per simulated patient for several settings (x-axis). The expected K number of components in logarithmic scale is indicated by the horizontal yellow line, while the observed number is reported on the y-axis. Along with K, each quadrant shows whether the simulated components tend to be uniform-like (αsim = 1) or low-overlapping (αsim = 1/M, where M is the number of genomic alterations). Plus, the color of the points represent if the HDMM was run to detect more uniform-like components (αHDMM = 1, in red) or more disjunct components (αHDMM = 1/M, in blue).</p

Clustering of genomic alterations provided by the HDMM on public AML data.

The table exhibits how one HDMM clusters all gene mutations and cytogenetic anomalies across one garbage component (column 0) and ten components (1-10). Some alterations are uniquely assigned to a single component but more than half are assigned at least to two components. Besides, this table shows that the genomic alterations are not equally abundant in the cohort with NPM1 being the most frequent occurring alteration. (CSV)</p