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aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Fibroglandular Tissue Segmentation in Breast MRI using Vision Transformers -- A multi-institutional evaluation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=M%C3%BCller-Franzes%2C+G">Gustav M眉ller-Franzes</a>, <a href="/search/cs?searchtype=author&query=M%C3%BCller-Franzes%2C+F">Fritz M眉ller-Franzes</a>, <a href="/search/cs?searchtype=author&query=Huck%2C+L">Luisa Huck</a>, <a href="/search/cs?searchtype=author&query=Raaff%2C+V">Vanessa Raaff</a>, <a href="/search/cs?searchtype=author&query=Kemmer%2C+E">Eva Kemmer</a>, <a href="/search/cs?searchtype=author&query=Khader%2C+F">Firas Khader</a>, <a href="/search/cs?searchtype=author&query=Arasteh%2C+S+T">Soroosh Tayebi Arasteh</a>, <a href="/search/cs?searchtype=author&query=Nolte%2C+T">Teresa Nolte</a>, <a href="/search/cs?searchtype=author&query=Kather%2C+J+N">Jakob Nikolas Kather</a>, <a href="/search/cs?searchtype=author&query=Nebelung%2C+S">Sven Nebelung</a>, <a href="/search/cs?searchtype=author&query=Kuhl%2C+C">Christiane Kuhl</a>, <a href="/search/cs?searchtype=author&query=Truhn%2C+D">Daniel Truhn</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2304.08972v1-abstract-short" style="display: inline;"> Accurate and automatic segmentation of fibroglandular tissue in breast MRI screening is essential for the quantification of breast density and background parenchymal enhancement. In this retrospective study, we developed and evaluated a transformer-based neural network for breast segmentation (TraBS) in multi-institutional MRI data, and compared its performance to the well established convolutiona… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.08972v1-abstract-full').style.display = 'inline'; document.getElementById('2304.08972v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.08972v1-abstract-full" style="display: none;"> Accurate and automatic segmentation of fibroglandular tissue in breast MRI screening is essential for the quantification of breast density and background parenchymal enhancement. In this retrospective study, we developed and evaluated a transformer-based neural network for breast segmentation (TraBS) in multi-institutional MRI data, and compared its performance to the well established convolutional neural network nnUNet. TraBS and nnUNet were trained and tested on 200 internal and 40 external breast MRI examinations using manual segmentations generated by experienced human readers. Segmentation performance was assessed in terms of the Dice score and the average symmetric surface distance. The Dice score for nnUNet was lower than for TraBS on the internal testset (0.909$\pm$0.069 versus 0.916$\pm$0.067, P<0.001) and on the external testset (0.824$\pm$0.144 versus 0.864$\pm$0.081, P=0.004). Moreover, the average symmetric surface distance was higher (=worse) for nnUNet than for TraBS on the internal (0.657$\pm$2.856 versus 0.548$\pm$2.195, P=0.001) and on the external testset (0.727$\pm$0.620 versus 0.584$\pm$0.413, P=0.03). Our study demonstrates that transformer-based networks improve the quality of fibroglandular tissue segmentation in breast MRI compared to convolutional-based models like nnUNet. These findings might help to enhance the accuracy of breast density and parenchymal enhancement quantification in breast MRI screening. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.08972v1-abstract-full').style.display = 'none'; document.getElementById('2304.08972v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Sci Rep 13, 14207 (2023) </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" 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