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Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[0.0.1] - 2023-11-08

First version of the library. This is the starting point of the development of the deepskin package. Further improvements will occur in the next versions.

Added

  • 🌐 [Global] Instruction for the pull-requests and issues with related templates

  • 🌐 [Global] List of requirements for the Python package

  • 🌐 [Global] Manifest and pyproject files for the Python package

  • 🌐 [Global] Add first version of Github Actions CI for Python and Docs

  • 💻 [Python] Installation file for the Python package

  • 💻 [Python] Entry point of the library for its usage with command line interface (ref. deepskin/__main__.py)

  • 💻 [Python] Automated versioning of the library via setup installation

  • 💻 [Python] Definition of deepskin feature classes and statistics

  • 💻 [Python] Insert model checkpoint download

  • 💻 [Python] First version of the entire feature extraction module

  • 💻 [Python] Move from binary to semantic segmentation using latest version of Deepskin db

  • 💻 [Python] Add command line usage for PWAT estimation

  • 💻 [Python] Add command line usage for image segmentation

  • 🚧 [Features] First list of deepskin features:

    • Color features: We extracted the average and standard deviation of RGB channels for each wound and peri-wound segmentation. This set of measures aims to quantify the appearance of the wound area in terms of redness and color heterogeneity.

      We converted each masked image into the corresponding HSV color space. For each channel, we extracted the average and standard deviation values. The HSV color space is more informative than the RGB one since it takes care of different light exposition (saturation). In this way, we monitored the various conditions in which the images were acquired.

      Both these two sets of features aim to quantify the necrotic tissue components of the wounds. The necrotic tissue, indeed, could be modeled as a darker component in the wound/peri-wound area, which alters the average color of the lesion. The Necrotic Tissue type and the Total Amount of Necrotic Tissue involve 2/8 items in the PWAT estimation.

    • Redness features: The primary information on the healing stage of a wound can be obtained by monitoring its redness (erythema) compared to the surrounding area. Several redness measurements are proposed in literature, belonging to different medical fields and applications. We extracted two measures of redness.

      The first measure was proposed by Park et al. 1_, and involves a combination of the RGB channels, i.e.,

      $$ Redness_{RGB} = 1/n \sum_{i=1}^n \frac{(2R_i - G_i - B_i)}{(2 \times (R_i + G_i + B_i))} $$

      Where R, G, and B are the red, green, and blue channels of the masked image, respectively, the n value represents the number of pixels in the considered mask. This measure emphasizes the R intensity using a weighted combination of the three RGB channels.

      The second measure was proposed by Amparo et al. 2_, and involves a combination of the HSV channels, i.e.,

      $$ Redness_{HSV} = 1/n \sum_{i=1}^n H_i \times S_i $$

      where H and S represent the hue and saturation intensities of the masked image, respectively. This measure tends to be more robust against different image light expositions.

      Both these features were extracted on the wound and peri-wound areas independently. Redness estimations could help to quantify the Peri-ulcer Skin Viability, Granulation Tissue Type, and Necrotic Tissue Type, which represent 3/8 items involved in the PWAT estimation.

    • Morphological features: We measured the morphological and textural characteristics of the wound and peri-wound areas by computing the 13 Haralick's features. Haralick's features are becoming standard texture descriptors in multiple medical image analyses, especially in the Radiomic research field. This set of features was evaluated on the grey-level co-occurrence matrix (GLCM) associated with the grayscale versions of the original images, starting from the areas identified by our segmentation models. We computed the 13 standard Haralick's features, given by energy, inertia, entropy, inverse difference moment, cluster shade, and cluster prominence. Using textural elements, we aimed to quantify information related to the Granulation Tissue types and Amount of Granulation Tissue, which are 2/8 items of the total PWAT score.

  • 📕 [Docs] First version of the README instructions

  • 📕 [Docs] First version of the Sphinx documentation

  • 📕 [Docs] First version of the Read-the-Docs documentation

  • 📕 [Docs] List of notebook examples in the sphinx documentation

  • 📕 [Docs] Notebook example for deepskin package features

  • 📕 [Docs] Notebook example for ASSL training model

  • 📕 [Docs] Notebook example for PWAT estimation