Praetorius Jan-Philipp, Walluks Kassandra, Svensson Carl-Magnus, Arnold Dirk, Figge Marc Thilo
Applied Systems Biology, Leibniz institute for natural Product Research and infection Biology - Hans Knöll institute (HKI), Jena, Germany.
Faculty of Biological Sciences, Friedrich Schiller University Jena, Jena, Germany.
Comput Struct Biotechnol J. 2023 Jul 25;21:3696-3704. doi: 10.1016/j.csbj.2023.07.031. eCollection 2023.
The assessment of muscle condition is of great importance in various research areas. In particular, evaluating the degree of intramuscular fat (IMF) in tissue sections is a challenging task, which today is still mostly performed qualitatively or quantitatively by a highly subjective and error-prone manual analysis. We here realize the mission to make automated IMF analysis possible that (i) minimizes subjectivity, (ii) provides accurate and quantitative results quickly, and (iii) is cost-effective using standard hematoxylin and eosin (H&E) stained tissue sections. To address all these needs in a deep learning approach, we utilized the convolutional encoder-decoder network SegNet to train the specialized network IMFSegNet allowing to accurately quantify the spatial distribution of IMF in histological sections. Our fully automated analysis was validated on 17 H&E-stained muscle sections from individual sheep and compared to various state-of-the-art approaches. Not only does IMFSegNet outperform all other approaches, but this neural network also provides fully automated and highly accurate results utilizing the most cost-effective procedures of sample preparation and imaging. Furthermore, we shed light on the opacity of black-box approaches such as neural networks by applying an explainable artificial intelligence technique to clarify that the success of IMFSegNet actually lies in identifying the hard-to-detect IMF structures. Embedded in our open-source visual programming language JIPipe that does not require programming skills, it can be expected that IMFSegNet advances muscle condition assessment in basic research across multiple areas as well as in research fields focusing on translational clinical applications.
肌肉状况评估在各个研究领域都具有重要意义。特别是，评估组织切片中的肌内脂肪（IMF）程度是一项具有挑战性的任务，目前大多仍通过高度主观且容易出错的手动分析进行定性或定量评估。我们在此实现了一项使命，即让自动化的IMF分析成为可能，这种分析（i）最大限度地减少主观性，（ii）快速提供准确的定量结果，（iii）使用标准苏木精和伊红（H&E）染色的组织切片具有成本效益。为了通过深度学习方法满足所有这些需求，我们利用卷积编码器 - 解码器网络SegNet训练了专门的网络IMFSegNet，以准确量化组织学切片中IMF的空间分布。我们的全自动分析在来自单个绵羊的17个H&E染色肌肉切片上得到验证，并与各种最先进的方法进行了比较。IMFSegNet不仅优于所有其他方法，而且该神经网络还利用最具成本效益的样品制备和成像程序提供全自动且高度准确的结果。此外，我们通过应用可解释人工智能技术来揭示神经网络等黑箱方法的不透明性，以阐明IMFSegNet的成功实际上在于识别难以检测的IMF结构。IMFSegNet嵌入我们的开源视觉编程语言JIPipe中，该语言不需要编程技能，可以预期IMFSegNet将推动多个基础研究领域以及专注于转化临床应用的研究领域中的肌肉状况评估。
