https://puma.ub.uni-stuttgart.de/group/simtech/compositionPUMA RSS feed for /group/simtech/composition2026-04-23T03:05:29+02:00https://puma.ub.uni-stuttgart.de/bibtex/2152183f0edd23d3a6b697219f2c6ca41/inspo5inspo52025-01-31T11:03:06+01:00imaging deep sports learning medicine body resonance system composition machine musculoskeletal magnetic <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Saied Ramedani" itemprop="url" href="/person/13afe397a451bce869962d016312aff6a/author/0"><span itemprop="name">S. Ramedani</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ebru Kelesoglu" itemprop="url" href="/person/13afe397a451bce869962d016312aff6a/author/1"><span itemprop="name">E. Kelesoglu</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Norman Stutzig" itemprop="url" href="/person/13afe397a451bce869962d016312aff6a/author/2"><span itemprop="name">N. Stutzig</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hendrik Von Tengg‐Kobligk" itemprop="url" href="/person/13afe397a451bce869962d016312aff6a/author/3"><span itemprop="name">H. Von Tengg‐Kobligk</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Keivan Daneshvar Ghorbani" itemprop="url" href="/person/13afe397a451bce869962d016312aff6a/author/4"><span itemprop="name">K. Daneshvar Ghorbani</span></a></span>, </span> und <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Tobias Siebert" itemprop="url" href="/person/13afe397a451bce869962d016312aff6a/author/5"><span itemprop="name">T. Siebert</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">Physiological Reports</span>, </em> </span>(<em><span>Januar 2025<meta content="Januar 2025" itemprop="datePublished"/></span></em>)</span>Fri Jan 31 11:03:06 CET 2025Physiological Reports013Quantification of training‐induced alterations in body composition via automated machine learning analysis of MRI images in the thigh region: A pilot study in young females132025imaging deep sports learning medicine body resonance system composition machine musculoskeletal magnetic AbstractThe maintenance of an appropriate ratio of body fat to muscle mass is essentialfor the preservation of health and performance, as excessive body fat is associatedwith an increased risk of various diseases. Accurate body composition assessmentrequires precise segmentation of structures. In this study we developed a novelautomatic machine learning approach for volumetric segmentation and quantita-tive assessment of MRI volumes and investigated the efficacy of using a machinelearning algorithm to assess muscle, subcutaneous adipose tissue (SAT), and bonevolume of the thigh before and after a strength training. Eighteen healthy, young,female volunteers were randomly allocated to two groups: intervention group(IG) and control group (CG). The IG group followed an 8-week strength endur-ance training plan that was conducted two times per week. Before and after thetraining, the subjects of both groups underwent MRI scanning. The evaluation ofthe image data was performed by a machine learning system which is based ona 3D U-Net- based Convolutional Neural Network. The volumes of muscle, bone,and SAT were each examined using a 2 (GROUP [IG vs. CG]) × 2 (TIME [pre-intervention vs. post-intervention]) analysis of variance (ANOVA) with repeatedmeasures for the factor TIME. The results of the ANOVA demonstrate significantTIME × GROUP interaction effects for the muscle volume (F1,16 = 12.80, p = 0.003,ηP2 = 0.44) with an increase of 2.93% in the IG group and no change in the CG(−0.62%, p = 0.893). There were no significant changes in bone or SAT volumebetween the groups. This study supports the use of artificial intelligence systemsto analyze MRI images as a reliable tool for monitoring training responses onbody composition.https://puma.ub.uni-stuttgart.de/bibtex/2346fb7cde84b5e8f820d71aa9d8e0ce0/inspo5inspo52022-07-19T11:10:29+02:00Stomach tension Biaxial staining Tissue composition muscle testingTissue Smooth Microstructure wall <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Melanie Bauer" itemprop="url" href="/person/12f5fd279259cd2399f3e049e26399fc7/author/0"><span itemprop="name">M. Bauer</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Enrique Morales-Orcajo" itemprop="url" href="/person/12f5fd279259cd2399f3e049e26399fc7/author/1"><span itemprop="name">E. Morales-Orcajo</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Lisa Klemm" itemprop="url" href="/person/12f5fd279259cd2399f3e049e26399fc7/author/2"><span itemprop="name">L. Klemm</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Seydewitz" itemprop="url" href="/person/12f5fd279259cd2399f3e049e26399fc7/author/3"><span itemprop="name">R. Seydewitz</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Victoria Fiebach" itemprop="url" href="/person/12f5fd279259cd2399f3e049e26399fc7/author/4"><span itemprop="name">V. Fiebach</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Tobias Siebert" itemprop="url" href="/person/12f5fd279259cd2399f3e049e26399fc7/author/5"><span itemprop="name">T. Siebert</span></a></span>, </span> und <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Markus Böl" itemprop="url" href="/person/12f5fd279259cd2399f3e049e26399fc7/author/6"><span itemprop="name">M. Böl</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">Acta Biomaterialia</span>, </em> </span>(<em><span>Januar 2020<meta content="Januar 2020" itemprop="datePublished"/></span></em>)</span>Tue Jul 19 11:10:29 CEST 2022Acta Biomaterialia0183--99Biomechanical and microstructural characterisation of the porcine stomach wall: Location- and layer-dependent investigations1022020Stomach tension Biaxial staining Tissue composition muscle testingTissue Smooth Microstructure wall