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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.00838">arXiv:2306.00838</a> <span> [<a href="https://arxiv.org/pdf/2306.00838">pdf</a>, <a href="https://arxiv.org/format/2306.00838">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Other Quantitative Biology">q-bio.OT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Moawad%2C+A+W">Ahmed W. Moawad</a>, <a href="/search/q-bio?searchtype=author&query=Janas%2C+A">Anastasia Janas</a>, <a href="/search/q-bio?searchtype=author&query=Baid%2C+U">Ujjwal Baid</a>, <a href="/search/q-bio?searchtype=author&query=Ramakrishnan%2C+D">Divya Ramakrishnan</a>, <a href="/search/q-bio?searchtype=author&query=Saluja%2C+R">Rachit Saluja</a>, <a href="/search/q-bio?searchtype=author&query=Ashraf%2C+N">Nader Ashraf</a>, <a href="/search/q-bio?searchtype=author&query=Jekel%2C+L">Leon Jekel</a>, <a href="/search/q-bio?searchtype=author&query=Amiruddin%2C+R">Raisa Amiruddin</a>, <a href="/search/q-bio?searchtype=author&query=Adewole%2C+M">Maruf Adewole</a>, <a href="/search/q-bio?searchtype=author&query=Albrecht%2C+J">Jake Albrecht</a>, <a href="/search/q-bio?searchtype=author&query=Anazodo%2C+U">Udunna Anazodo</a>, <a href="/search/q-bio?searchtype=author&query=Aneja%2C+S">Sanjay Aneja</a>, <a href="/search/q-bio?searchtype=author&query=Anwar%2C+S+M">Syed Muhammad Anwar</a>, <a href="/search/q-bio?searchtype=author&query=Bergquist%2C+T">Timothy Bergquist</a>, <a href="/search/q-bio?searchtype=author&query=Calabrese%2C+E">Evan Calabrese</a>, <a href="/search/q-bio?searchtype=author&query=Chiang%2C+V">Veronica Chiang</a>, <a href="/search/q-bio?searchtype=author&query=Chung%2C+V">Verena Chung</a>, <a href="/search/q-bio?searchtype=author&query=Conte%2C+G+M+M">Gian Marco Marco Conte</a>, <a href="/search/q-bio?searchtype=author&query=Dako%2C+F">Farouk Dako</a>, <a href="/search/q-bio?searchtype=author&query=Eddy%2C+J">James Eddy</a>, <a href="/search/q-bio?searchtype=author&query=Ezhov%2C+I">Ivan Ezhov</a>, <a href="/search/q-bio?searchtype=author&query=Familiar%2C+A">Ariana Familiar</a>, <a href="/search/q-bio?searchtype=author&query=Farahani%2C+K">Keyvan Farahani</a>, <a href="/search/q-bio?searchtype=author&query=Iglesias%2C+J+E">Juan Eugenio Iglesias</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Z">Zhifan Jiang</a> , et al. (206 additional authors not shown) </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="2306.00838v2-abstract-short" style="display: inline;"> The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and chara… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.00838v2-abstract-full').style.display = 'inline'; document.getElementById('2306.00838v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.00838v2-abstract-full" style="display: none;"> The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space.The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.00838v2-abstract-full').style.display = 'none'; document.getElementById('2306.00838v2-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> 17 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.17033">arXiv:2305.17033</a> <span> [<a href="https://arxiv.org/pdf/2305.17033">pdf</a>, <a href="https://arxiv.org/format/2305.17033">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs) </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Kazerooni%2C+A+F">Anahita Fathi Kazerooni</a>, <a href="/search/q-bio?searchtype=author&query=Khalili%2C+N">Nastaran Khalili</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+X">Xinyang Liu</a>, <a href="/search/q-bio?searchtype=author&query=Haldar%2C+D">Debanjan Haldar</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Z">Zhifan Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Anwar%2C+S+M">Syed Muhammed Anwar</a>, <a href="/search/q-bio?searchtype=author&query=Albrecht%2C+J">Jake Albrecht</a>, <a href="/search/q-bio?searchtype=author&query=Adewole%2C+M">Maruf Adewole</a>, <a href="/search/q-bio?searchtype=author&query=Anazodo%2C+U">Udunna Anazodo</a>, <a href="/search/q-bio?searchtype=author&query=Anderson%2C+H">Hannah Anderson</a>, <a href="/search/q-bio?searchtype=author&query=Bagheri%2C+S">Sina Bagheri</a>, <a href="/search/q-bio?searchtype=author&query=Baid%2C+U">Ujjwal Baid</a>, <a href="/search/q-bio?searchtype=author&query=Bergquist%2C+T">Timothy Bergquist</a>, <a href="/search/q-bio?searchtype=author&query=Borja%2C+A+J">Austin J. Borja</a>, <a href="/search/q-bio?searchtype=author&query=Calabrese%2C+E">Evan Calabrese</a>, <a href="/search/q-bio?searchtype=author&query=Chung%2C+V">Verena Chung</a>, <a href="/search/q-bio?searchtype=author&query=Conte%2C+G">Gian-Marco Conte</a>, <a href="/search/q-bio?searchtype=author&query=Dako%2C+F">Farouk Dako</a>, <a href="/search/q-bio?searchtype=author&query=Eddy%2C+J">James Eddy</a>, <a href="/search/q-bio?searchtype=author&query=Ezhov%2C+I">Ivan Ezhov</a>, <a href="/search/q-bio?searchtype=author&query=Familiar%2C+A">Ariana Familiar</a>, <a href="/search/q-bio?searchtype=author&query=Farahani%2C+K">Keyvan Farahani</a>, <a href="/search/q-bio?searchtype=author&query=Haldar%2C+S">Shuvanjan Haldar</a>, <a href="/search/q-bio?searchtype=author&query=Iglesias%2C+J+E">Juan Eugenio Iglesias</a>, <a href="/search/q-bio?searchtype=author&query=Janas%2C+A">Anastasia Janas</a> , et al. (48 additional authors not shown) </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="2305.17033v7-abstract-short" style="display: inline;"> Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCA… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.17033v7-abstract-full').style.display = 'inline'; document.getElementById('2305.17033v7-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.17033v7-abstract-full" style="display: none;"> Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.17033v7-abstract-full').style.display = 'none'; document.getElementById('2305.17033v7-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> 23 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.08350">arXiv:2211.08350</a> <span> [<a href="https://arxiv.org/pdf/2211.08350">pdf</a>, <a href="https://arxiv.org/format/2211.08350">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Motor imagery classification using EEG spectrograms </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Khan%2C+S+U">Saadat Ullah Khan</a>, <a href="/search/q-bio?searchtype=author&query=Majid%2C+M">Muhammad Majid</a>, <a href="/search/q-bio?searchtype=author&query=Anwar%2C+S+M">Syed Muhammad Anwar</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="2211.08350v1-abstract-short" style="display: inline;"> The loss of limb motion arising from damage to the spinal cord is a disability that could effect people while performing their day-to-day activities. The restoration of limb movement would enable people with spinal cord injury to interact with their environment more naturally and this is where a brain-computer interface (BCI) system could be beneficial. The detection of limb movement imagination (… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.08350v1-abstract-full').style.display = 'inline'; document.getElementById('2211.08350v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.08350v1-abstract-full" style="display: none;"> The loss of limb motion arising from damage to the spinal cord is a disability that could effect people while performing their day-to-day activities. The restoration of limb movement would enable people with spinal cord injury to interact with their environment more naturally and this is where a brain-computer interface (BCI) system could be beneficial. The detection of limb movement imagination (MI) could be significant for such a BCI, where the detected MI can guide the computer system. Using MI detection through electroencephalography (EEG), we can recognize the imagination of movement in a user and translate this into a physical movement. In this paper, we utilize pre-trained deep learning (DL) algorithms for the classification of imagined upper limb movements. We use a publicly available EEG dataset with data representing seven classes of limb movements. We compute the spectrograms of the time series EEG signal and use them as an input to the DL model for MI classification. Our novel approach for the classification of upper limb movements using pre-trained DL algorithms and spectrograms has achieved significantly improved results for seven movement classes. When compared with the recently proposed state-of-the-art methods, our algorithm achieved a significant average accuracy of 84.9% for classifying seven movements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.08350v1-abstract-full').style.display = 'none'; document.getElementById('2211.08350v1-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> 15 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to ISBI 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2009.02903">arXiv:2009.02903</a> <span> [<a href="https://arxiv.org/pdf/2009.02903">pdf</a>, <a href="https://arxiv.org/format/2009.02903">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Brain Tumor Survival Prediction using Radiomics Features </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Yousaf%2C+S">Sobia Yousaf</a>, <a href="/search/q-bio?searchtype=author&query=Anwar%2C+S+M">Syed Muhammad Anwar</a>, <a href="/search/q-bio?searchtype=author&query=RaviPrakash%2C+H">Harish RaviPrakash</a>, <a href="/search/q-bio?searchtype=author&query=Bagci%2C+U">Ulas Bagci</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="2009.02903v1-abstract-short" style="display: inline;"> Surgery planning in patients diagnosed with brain tumor is dependent on their survival prognosis. A poor prognosis might demand for a more aggressive treatment and therapy plan, while a favorable prognosis might enable a less risky surgery plan. Thus, accurate survival prognosis is an important step in treatment planning. Recently, deep learning approaches have been used extensively for brain tumo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.02903v1-abstract-full').style.display = 'inline'; document.getElementById('2009.02903v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2009.02903v1-abstract-full" style="display: none;"> Surgery planning in patients diagnosed with brain tumor is dependent on their survival prognosis. A poor prognosis might demand for a more aggressive treatment and therapy plan, while a favorable prognosis might enable a less risky surgery plan. Thus, accurate survival prognosis is an important step in treatment planning. Recently, deep learning approaches have been used extensively for brain tumor segmentation followed by the use of deep features for prognosis. However, radiomics-based studies have shown more promise using engineered/hand-crafted features. In this paper, we propose a three-step approach for multi-class survival prognosis. In the first stage, we extract image slices corresponding to tumor regions from multiple magnetic resonance image modalities. We then extract radiomic features from these 2D slices. Finally, we train machine learning classifiers to perform the classification. We evaluate our proposed approach on the publicly available BraTS 2019 data and achieve an accuracy of 76.5% and precision of 74.3% using the random forest classifier, which to the best of our knowledge are the highest reported results yet. Further, we identify the most important features that contribute in improving the prediction. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.02903v1-abstract-full').style.display = 'none'; document.getElementById('2009.02903v1-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> 7 September, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to RNO Workshop at MICCAI 2020</span> </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 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