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format-standard has-post-thumbnail hentry category-brain-tumor tag-4d-segmentation tag-atlas-based-segmentation tag-automated-segmentation tag-brain-tumor-segmentation tag-deep-learning tag-glioblastoma tag-machine-learning tag-mri tag-multi-modal-imaging tag-probabilistic-models" itemtype="https://schema.org/CreativeWork" itemscope> <div class="inside-article"> <div class="featured-image page-header-image-single "> <img width="1200" height="628" src="https://cancerscience.net/archive/wp-content/uploads/2024/09/banner-01-min-scaled-e1727521667940.jpg" class="attachment-full size-full" alt="" itemprop="image" decoding="async" fetchpriority="high" /> </div> <header class="entry-header"> <h1 class="entry-title" itemprop="headline">Advancements in Brain Tumor Segmentation Techniques</h1> <div class="entry-meta"> <span class="posted-on"><time class="updated" datetime="2024-09-28T16:37:58+05:30" itemprop="dateModified">September 28, 2024</time><time class="entry-date published" datetime="2024-09-28T12:09:37+05:30" itemprop="datePublished">September 28, 2024</time></span> <span class="byline">by <span class="author vcard" itemprop="author" itemtype="https://schema.org/Person" itemscope><a class="url fn n" href="https://cancerscience.net/archive/author/cancerscience/" title="View all posts by cancerscience" rel="author" itemprop="url"><span class="author-name" itemprop="name">cancerscience</span></a></span></span> </div> </header> <div class="entry-content" itemprop="text"> <h3><b>Introduction</b></h3> <p><span style="font-weight: 400;">Detection of the lesion, which in the case of brain tumors is often referred to as segmentation is essential in neuroimaging for diagnoses, therapy planning, and assessment of the progress of the illness, especially gliomas, which are some of the most aggressive and prevalent kinds of brain tumors. In the last couple of decades, improvements in the methods for tumor segmentation especially in the brain, have been accomplished based on MRI. These have evolved from previously used traditional methods of segmentation to more advanced forms of automation-propelled segmentation through the application of machine learning and AI. The development of those techniques not only enhanced the precision and speed of tumor segmentation but also proved promising in terms of its reflection on the patient’s outcome. This article reviews the recent literature and describes the state of the art in the BID approach, various techniques of Brain tumor segmentation, and their future scopes.</span></p> <h3><b>Evolution of Brain Tumor Segmentation Techniques</b></h3> <p><span style="font-weight: 400;">Brain tumor segmentation was initially performed manually, which the radiologists and neurosurgeons used to segment areas of the tumor on the MRI images. Although M mode proved to be the most accurate way of segmenting the images, it was also time-consuming, cumbersome, and experienced large inter-observer variability. With that realization that patterns could not hold up to newer, more stringent tests for reliability and reproducibility, new semi-automated techniques were designed with the incorporation of edge detection and thresholding techniques. These methods, however, were semi-automatic and demanded much human interaction and contravariance in the appearance of the tumor in different patients.</span></p> <p><span style="font-weight: 400;">The following big steps forward were connected with the popularization of automated segmentation methods. These methods employed statistical models and machine learning algorithms to analyze tumor shapes with as little input from human operators as possible. From them, Gaussian mixture models and Markov random fields are often used because the shapes and intensities of tumors are usually variable. However, these methods have their drawbacks when it comes to segmenting tumors into three different types of regions, taking into account the heterogeneity of the tumors in question, such as glioblastomas.</span></p> <p></div></div> <div style="background: #f7f7f7;border: 1px solid rgba(0, 0, 0, 0.07);"> <div style="padding: 30px;"><div class="Adblock-main"> <div class="Adblock-head"> <h2>Yearwise Publication Trend on <b>“<a href="https://cancerscience.net/publication-trends/index/brain tumor" target="_blank" title="brain tumor - yearwise publication trends">brain tumor</a>”</b></h2> </div> </div><div class="results-container"><div class="chart-block" style="padding:15px;"> <div class="left"> <div id="results" class="results"></div> </div> <div class="right"> <div class="chart-container"><canvas id="publicationChart"></canvas></div> </div> <div class="keywordsdiv"> <div style="text-align:center;"><b>Find publication trends on relevant topics</b> </div> <span class="gp-icon icon-tags"><svg viewBox="0 0 512 512" aria-hidden="true" xmlns="http://www.w3.org/2000/svg" width="1em" height="1em"><path d="M20 39.5c-8.836 0-16 7.163-16 16v176c0 4.243 1.686 8.313 4.687 11.314l224 224c6.248 6.248 16.378 6.248 22.626 0l176-176c6.244-6.244 6.25-16.364.013-22.615l-223.5-224A15.999 15.999 0 00196.5 39.5H20zm56 96c0-13.255 10.745-24 24-24s24 10.745 24 24-10.745 24-24 24-24-10.745-24-24z"></path><path d="M259.515 43.015c4.686-4.687 12.284-4.687 16.97 0l228 228c4.686 4.686 4.686 12.284 0 16.97l-180 180c-4.686 4.687-12.284 4.687-16.97 0-4.686-4.686-4.686-12.284 0-16.97L479.029 279.5 259.515 59.985c-4.686-4.686-4.686-12.284 0-16.97z"></path></svg></span> <span id="keyword-stats"></span> </div> </div></div></div><div class="inside-article"><style> table { margin: 0 0 1.5em; 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if (!statistics || Object.keys(statistics).length === 0) { resultsContainer.innerHTML = '<p>No data found.</p>'; return; } var tableHTML = `<div class='pub-scroll'> <table class='tablediv' border='1' cellspacing='0' cellpadding='0'> <tr> <th>Year</th> <th>Publication Count</th> </tr>`; Object.entries(statistics).sort(([yearA], [yearB]) => yearB - yearA).forEach(([year, count]) => { const displayCount = count === 0 ? 'NA' : count; tableHTML += ` <tr> <td>${year}</td> <td>${displayCount}</td> </tr> `; }); tableHTML += '</table></div>'; resultsContainer.innerHTML = tableHTML; } function displayLineChart(statistics) { var years = Object.keys(statistics); var counts = Object.values(statistics); var ctx = document.getElementById('publicationChart').getContext('2d'); // Destroy existing chart instance if it exists if (chart !== null) { chart.destroy(); } // Create a new chart instance chart = new Chart(ctx, { type: 'line', data: { labels: years, datasets: [{ label: 'Publication Counts', data: counts, fill: false, borderColor: 'rgba(75, 192, 192, 1)', tension: 0.1 }] }, options: { scales: { y: { beginAtZero: true } }, plugins: { legend: { display: true, position: 'top' } } } }); } function displayKeywordStats(keywords) { var resultsContainer = document.getElementById('keyword-stats'); resultsContainer.innerHTML = ''; if (!keywords || keywords.length === 0) { resultsContainer.innerHTML = '<p>No data found.</p>'; return; } var keywordHTML = ''; keywords.forEach((key, index) => { let key_replace = key.replace(/ /g, '-'); key_replace = key_replace.toLowerCase(); keywordHTML += `<a href="https://cancerscience.net/publication-trends/index/${key_replace}" target="_blank" title="${key} - yearwise publication trends">${key}</a>`; if (index < keywords.length - 1) { keywordHTML += ', '; } }); resultsContainer.innerHTML = keywordHTML; } // Call the function with the PHP data var statistics = { "2014": 820, "2015": 887, "2016": 1196, "2017": 1045, "2018": 1315, "2019": 2553, "2020": 3301, "2021": 4171, "2022": 5436, "2023": 8955, "2024": 3153 }; var keywordsArray = ["Brain Tumor Segmentation","Deep Learning","Machine Learning","Atlas-based Segmentation","Multi-modal Imaging","Probabilistic Models","4D Segmentation","MRI","Glioblastoma","Automated Segmentation"]; displayResults(statistics); displayLineChart(statistics); displayKeywordStats(keywordsArray); </script></p> <h3><b>Machine Learning and AI in Brain Tumor Segmentation</b></h3> <p><span style="font-weight: 400;">With the help of machine learning, especially deep learning, the problem of segmenting brain tumors has undergone some dynamic changes. CNNs have been leading this revolution for the past few years and remarkably accurately segmenting tumors from MRI scans. CNNs are especially good at identifying spatial hierarchies in images and therefore are well-suited for the challenges of segmentation of brain tumors. Compared with the manually designed features of the traditional approaches, CNNs can learn all the features by themselves, which leads to a higher precision and robustness of the segmentation.</span></p> <p><span style="font-weight: 400;">There are many discoveries made in this area, one of which is the employment of multi-modal information in segmentation. The malignant brain tumors have a complex structure and could have different characteristics in different regions of the lesion (like the necrotic core, the enhancing tumor, and the edema), which could be seen with different contrasts in MRI sequences (as T1, T2, and FLAIR images). These different sequences are employed in multi-modal approaches that present the tumor from a broader perspective; hence, a more accurate segmentation is produced. Methods such as deep learning models like U-Net and its variants have been developed for multi-modal datasets to give very high accuracy in segmentation.</span></p> <p><span style="font-weight: 400;">One of the most important innovations reported is the combination of probabilistic models with deep learning. That is why these hybrid models, which combine the positive features of the probabilistic models (including uncertainty estimates) and the possibilities of the deep learning approaches (providing high accuracy), can present segmentation results of higher reliability. For example, constructing uncertainty into their predictions, Bayesian neural networks have been tested in the identification of brain tumors and segmentation of the same by not only presenting different segmentation results but also the uncertainty map of those particular images.</span></p> <p><span style="font-weight: 400;">Among the methods that were developed during the last years, atlas-based segmentation can be distinguished. This method involves the placement of annotations or ‘landmarks’ of a pre-segmented atlas (which is like a template brain) on a patient’s MRI scan. The atlas is then warped to consider the patient’s morphology, and the tumor is detected based on the result of such a warping. Atlas-based methods are particularly important in those cases when the tumor distorts the anatomy significantly, for instance in glioblastomas. These methods have been improved by employing the machine learning technique, wherein during the deformation process the model is given probabilities of the tumor-related changes in the anatomy of the brain.</span></p> <p><span style="font-weight: 400;">Another technique is based on the graph level and can be used for the segmentation of tumors. With this approach, the image is represented as a graph, with its nodes being the pixels or regions of an image, which have the edges between them reflecting their degree of similarity. In the segmentation process, it is crucial to find a cut in this graph that will separate the tumor from the rest of the brain. Both of these approaches are especially useful in the management of spatial topology between different regions of the tumor and the surrounding brain. Graph-based methods combined with deep learning have improved the results even more, and thus they can be used to segment brain tumors.</span></p> <p><span style="font-weight: 400;">To the rest of these, there is growing literature on the analysis of brain tumors and in the development of methods that can cater to the longitudinal analysis of the disease. Historical review means that the morphology of a tumor is evaluated over time, which is important in monitoring the effects of treatment in cancer management. The standard approaches to segmentation fail at this step because the appearance of the tumor significantly differs between time points. But what has emerged recently are the 4D segmentation techniques that input the temporal information to it and thereby help to trace the tumor changes more consistently and accurately.</span></p> <p></div></div> <div style="background: #f7f7f7;border: 1px solid rgba(0, 0, 0, 0.07);"> <div style="padding: 30px;"><div class="Adblock-main"> <div class="Adblock-head"> <h2>Recent Publications on <b>“<a href="https://cancerscience.net/recent-publications/index/brain tumor" target="_blank" rel="noopener" title="brain tumor - yearwise publication list">brain tumor</a>”</b></h2> </div> </div> <div class="pb-main"><div class="article-scroll"><div id="results_recent" class="results"></div></div><div class="keywordsdiv" style="margin: 0px 15px;margin-top:20px;"> <div style="text-align:center;"><b>Find publications on relevant topics</b> </div> <span class="gp-icon icon-tags"><svg viewBox="0 0 512 512" aria-hidden="true" xmlns="http://www.w3.org/2000/svg" width="1em" height="1em"><path d="M20 39.5c-8.836 0-16 7.163-16 16v176c0 4.243 1.686 8.313 4.687 11.314l224 224c6.248 6.248 16.378 6.248 22.626 0l176-176c6.244-6.244 6.25-16.364.013-22.615l-223.5-224A15.999 15.999 0 00196.5 39.5H20zm56 96c0-13.255 10.745-24 24-24s24 10.745 24 24-10.745 24-24 24-24-10.745-24-24z"></path><path d="M259.515 43.015c4.686-4.687 12.284-4.687 16.97 0l228 228c4.686 4.686 4.686 12.284 0 16.97l-180 180c-4.686 4.687-12.284 4.687-16.97 0-4.686-4.686-4.686-12.284 0-16.97L479.029 279.5 259.515 59.985c-4.686-4.686-4.686-12.284 0-16.97z"></path></svg></span> <span id="keyword-papers"></span> </div></div></div><div class="inside-article"> <style> .pb-main{ border: solid 1px #ccc; border-top: none; margin-bottom: 20px; padding-bottom: 25px; background:#fff; } .author-main { border: solid 1px #ccc; border-top: none; margin-bottom: 20px; padding-bottom: 25px; background:#fff; } .publication-block { padding: 10px; margin-bottom: 10px; background-color: #f9f9f9; text-align: left; background: #FFF; border-bottom: solid 1px #ccc; margin-left: 15px; margin-right: 15px; } .publication-block h3 { margin: 0 0 10px; color: #000!important; } .publication-block a { font-size: 16px !important; line-height: 1em; font-weight: 600; text-transform: none; color: #000; padding: 0px; } .publication-block a:hover{ color: #227cdc; text-decoration:underline; } .article-scroll { max-height: 445px; overflow-y: auto; overflow-x: hidden; } ::-webkit-scrollbar-track { -webkit-box-shadow: inset 0 0 6px rgba(0,0,0,0.3); background-color: #efefef; border-radius:30px; } ::-webkit-scrollbar { width: 6px; background-color: #efefef; border-radius:30px; } ::-webkit-scrollbar-thumb { background-color: #ababab; border-radius:30px; } .publication-block p { margin-bottom: .5em; font-size: 15px; color: #000; } h3 { font-size: 18px !important; margin-bottom: 20px; line-height: 1.2em; font-weight: 600; text-transform: none; } a { padding: 5px; color: #a71c49; } #keyword-papers{ margin-top: 20px; text-align: center; } </style> <script> function displayResults_recent(papers) { var resultsContainer = document.getElementById('results_recent'); if (!papers || papers.length === 0) { resultsContainer.innerHTML = '<p>No recent publications found.</p>'; return; } papers.forEach(paper => { var publicationBlock = document.createElement('div'); publicationBlock.className = 'publication-block'; var publicationHTML = ` <div style="margin-bottom: 10px;line-height: 24px;"><a href="${paper.url}" target="_blank" title="${paper.title}">${paper.title}</a></div> <p><strong>Issue Release:</strong> ${paper.publishedDate}</p> `; publicationBlock.innerHTML = publicationHTML; resultsContainer.appendChild(publicationBlock); }); } function displayKeywordPapers(keywords) { var resultsContainer = document.getElementById('keyword-papers'); resultsContainer.innerHTML = ''; if (!keywords || keywords.length === 0) { resultsContainer.innerHTML = '<p>No data found.</p>'; return; } var keywordHTML = ''; keywords.forEach((key, index) => { let key_replace = key.replace(/ /g, '-'); key_replace = key_replace.toLowerCase(); keywordHTML += `<a href="https://cancerscience.net/recent-publications/index/${key_replace}" target="_blank" title="${key} - publication list">${key}</a>`; if (index < keywords.length - 1) { keywordHTML += ', '; } }); resultsContainer.innerHTML = keywordHTML; } // Call the function with the PHP data var recent_papers = [ { "title": "A Rare Case of Thoracic SMARCA4-Deficient Undifferentiated Tumor With Diffuse Brain Metastasis.", "url": "https:\/\/pubmed.ncbi.nlm.nih.gov\/38947666", "publishedDate": "2024" }, { "title": "Identification of TNFAIP6 as a reliable prognostic indicator of low-grade glioma.", "url": "https:\/\/pubmed.ncbi.nlm.nih.gov\/38948040", "publishedDate": "2024" }, { "title": "The CCL2-CCR4 Axis Promotes Regulatory T Cell Trafficking to Canine Glioma Tissues.", "url": "https:\/\/pubmed.ncbi.nlm.nih.gov\/38947002", "publishedDate": "2024" }, { "title": "Cervical Lymph Node Metastasis From Medulloblastoma in a Young Adult: Case Report and Literature Review.", "url": "https:\/\/pubmed.ncbi.nlm.nih.gov\/38947611", "publishedDate": "2024" }, { "title": "Paraneoplastic neuromyelitis optica spectrum disorder associated with ovarian dysgerminoma: a case report and literature review.", "url": "https:\/\/pubmed.ncbi.nlm.nih.gov\/38947316", "publishedDate": "2024" }, { "title": "Investigation of the effects of curcumin and piperine on cyclophosphamide-induced brain injury in rats.", "url": "https:\/\/pubmed.ncbi.nlm.nih.gov\/38953502", "publishedDate": "2024" }, { "title": "Using a three-dimensional-printed device with the patient\\\\\\'s maxillary dental impression allows to perform minimally invasive brain biopsies in dogs and cats: a preliminary study.", "url": "https:\/\/pubmed.ncbi.nlm.nih.gov\/38955214", "publishedDate": "2024" }, { "title": "[Tumor-Like Primary Central Nervous System Vasculitis With Spina Involvement:Report of One Case].", "url": "https:\/\/pubmed.ncbi.nlm.nih.gov\/38953273", "publishedDate": "2024" }, { "title": "Utilizing human cerebral organoids to model breast cancer brain metastasis in culture.", "url": "https:\/\/pubmed.ncbi.nlm.nih.gov\/38951862", "publishedDate": "2024" }, { "title": "Recent advances in gene delivery nanoplatforms based on spherical nucleic acids.", "url": "https:\/\/pubmed.ncbi.nlm.nih.gov\/38951806", "publishedDate": "2024" }, { "title": "[Calcium Dibutyryl Adenosine Cyclophosphate Enhances the Effect of Metoprolol in Treating Older Adults With Heart Failure Combined With Arrhythmia].", "url": "https:\/\/pubmed.ncbi.nlm.nih.gov\/38948272", "publishedDate": "2024" }, { "title": "Capmatinib is an effective treatment for MET-fusion driven pediatric high-grade glioma and synergizes with radiotherapy.", "url": "https:\/\/pubmed.ncbi.nlm.nih.gov\/38849845", "publishedDate": "2024" }, { "title": "Charge-switchable cell-penetrating peptides for rerouting nanoparticles to glioblastoma treatment.", "url": "https:\/\/pubmed.ncbi.nlm.nih.gov\/38850741", "publishedDate": "2024" }, { "title": "SRSF9 promotes cell proliferation and migration of glioblastoma through enhancing CDK1 expression.", "url": "https:\/\/pubmed.ncbi.nlm.nih.gov\/38842611", "publishedDate": "2024" }, { "title": "Alzheimer\\\\\\'s disease and drug delivery across the blood-brain barrier: approaches and challenges.", "url": "https:\/\/pubmed.ncbi.nlm.nih.gov\/38849950", "publishedDate": "2024" }, { "title": "Recent advances in breast cancer metastasis with special emphasis on metastasis to the brain.", "url": "https:\/\/pubmed.ncbi.nlm.nih.gov\/38850880", "publishedDate": "2024" }, { "title": "Effects of protein-enriched nutritional support on skeletal muscle mass and rehabilitative outcomes in brain tumor patients: a randomized controlled trial.", "url": "https:\/\/pubmed.ncbi.nlm.nih.gov\/38839858", "publishedDate": "2024" }, { "title": "Assessing tumor volumetric reduction with consideration for setup errors based on mathematical tumor model and microdosimetric kinetic model in single-isocenter VMAT for brain metastases.", "url": "https:\/\/pubmed.ncbi.nlm.nih.gov\/38884671", "publishedDate": "2024" }, { "title": "Programmed cell death disrupts inflammatory tumor microenvironment (TME) and promotes glioblastoma evolution.", "url": "https:\/\/pubmed.ncbi.nlm.nih.gov\/38890642", "publishedDate": "2024" }, { "title": "Pediatric-type diffuse low-grade glioma with T2-FLAIR mismatch sign: a case report and literature review.", "url": "https:\/\/pubmed.ncbi.nlm.nih.gov\/38884778", "publishedDate": "2024" } ]; var keywordsArray = ["Brain Tumor Segmentation","Deep Learning","Machine Learning","Atlas-based Segmentation","Multi-modal Imaging","Probabilistic Models","4D Segmentation","MRI","Glioblastoma","Automated Segmentation"]; displayResults_recent(recent_papers); displayKeywordPapers(keywordsArray); </script></p> <h3><b>Challenges and Future Directions</b></h3> <p><span style="font-weight: 400;">Nevertheless, some issues are still to be encountered when it comes to the segmentation of brain tumors: By far the greatest obstacle, however, is the fact that tumors can look different in different patients and can also change over time in the same patient. This variability is partly the reason why it becomes easy to model datasets with little transfer from one set to another. Further, there are not many big annotated data sets that can be used to train deep learning algorithms to improve segmentation methods. Even though challenges like Brain Tumor Image Segmentation (BraTS) have released significant datasets out there, the models still lack proper and diverse data.</span></p> <p><span style="font-weight: 400;">Another issue that is also noteworthy is the application of these segregation methods in clinical work. Even though the ADV of automated segmentation methods has been promising in research settings, their implementation in clinical settings has been less. This is partly due to the obligatory validation and the necessity of these methods to integrate into clinical practice. In addition, what has been a major issue with deep learning models is their interpretability. There is also the need to build clinician’s confidence and trust in the decisions made by these models. which can be done by developing Explainable Artificial Intelligence (XAI) techniques that can help clinicians understand why a particular segmentation decision was made.</span></p> <p><span style="font-weight: 400;">As for future work, brain tumor segmentation will further develop based on the eloquent combination of AI and nerves and neuroradiology expertise. To address the current issues, one can expect the emergence of models that incorporate the best features of several approaches to segmentation. Moreover, the idea of federative learning, when the models are trained using the data of different institutions but the data itself is not shared, may help to overcome the problem of the lack of data while maintaining the patient’s confidentiality. These technologies still have the potential to not only enhance the features of the segmentation of brain tumors but also have the potential to change how brain tumors are diagnosed and treated.</span></p> <h3><b>Conclusion</b></h3> <p><span style="font-weight: 400;">The area of interest for brain tumor segmentation has evolved significantly in the past few decades due to the spur in machine learning and AI technologies. This paper presents a field that has experienced stunning advancement and development of both manual and automatic approaches to segmenting tumors, ranging from simple omission methods to complex automated techniques that have enhanced precision, consistency, and reliability in segmenting tumors. Although some limitations need to be addressed, the progress of the integration of imaging strains and deep learning models into oncology, as well as the integration of oncologists’ knowledge and expertise into the models, provides an optimistic view of the progress of improving brain tumor segmentation and helping patients as many times as possible. Thus, as these technologies advance and are further incorporated into practice in the treatment of this disease, these technologies can further transform the diagnosis and treatment of primary brain tumors.</span></p> <p></p> <h3><b>References</b></h3> <ol> <li>Porz, N., Bauer, S., Pica, A., Schucht, P., Beck, J., Verma, R.K., Slotboom, J., Reyes, M. and Wiest, R., 2014. <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0096873">Multi-modal glioblastoma segmentation: man versus machine.</a> <i>PloS one</i>, <i>9</i>(5), p.e96873.</li> <li>Bauer, S., Wiest, R., Nolte, L.P. and Reyes, M., 2013. <a href="https://iopscience.iop.org/article/10.1088/0031-9155/58/13/R97/meta">A survey of MRI-based medical image analysis for brain tumor studies.</a> <i>Physics in Medicine & Biology</i>, <i>58</i>(13), p.R97.</li> <li>Gooya, A., Pohl, K.M., Bilello, M., Cirillo, L., Biros, G., Melhem, E.R. and Davatzikos, C., 2012. <a href="https://ieeexplore.ieee.org/abstract/document/6266750">GLISTR: glioma image segmentation and registration.</a> <i>IEEE transactions on medical imaging</i>, <i>31</i>(10), pp.1941-1954.</li> <li>Hamamci, A., Kucuk, N., Karaman, K., Engin, K. and Unal, G., 2011. <a href="https://ieeexplore.ieee.org/abstract/document/6112681">Tumor-cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications.</a> <i>IEEE transactions on medical imaging</i>, <i>31</i>(3), pp.790-804.</li> <li>Shin, H.C., Orton, M.R., Collins, D.J., Doran, S.J. and Leach, M.O., 2012. <a href="https://ieeexplore.ieee.org/abstract/document/6399478">Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data.</a> <i>IEEE transactions on pattern analysis and machine intelligence</i>, <i>35</i>(8), pp.1930-1943.</li> <li>Artaechevarria, X., Munoz-Barrutia, A. and Ortiz-de-Solorzano, C., 2009. <a href="https://ieeexplore.ieee.org/abstract/document/4785214">Combination strategies in multi-atlas image segmentation: application to brain MR data.</a> <i>IEEE transactions on medical imaging</i>, <i>28</i>(8), pp.1266-1277.</li> <li>Shin, H.C., Orton, M., Collins, D.J., Doran, S. and Leach, M.O., 2011, December. <a href="https://ieeexplore.ieee.org/abstract/document/6146980">Autoencoder in time-series analysis for unsupervised tissues characterisation in a large unlabelled medical image dataset.</a> In <i>2011 10th international conference on machine learning and applications and workshops</i> (Vol. 1, pp. 259-264). IEEE.</li> </ol> <p></div></div> <div style="background: #f7f7f7;border: 1px solid rgba(0, 0, 0, 0.07);"> <div style="padding: 30px;"><div class="Adblock-main"> <div class="Adblock-head"> <h2>Top Experts on “<b style="color:#000;font-size:22px;">brain tumor</b>“</h2> </div> </div><div class="author-main"><div id="results_author"></div><div style="text-align: center;"><a class="register-button" href="https://cancerscience.net/expert-search" target="_blank" rel="noopener">Find experts on any field</a></div></div><div class="inside-article" style="background: none;border: none;box-shadow: none;margin-top: -70px;"> <style> .author-block { padding: 15px; margin-bottom: 10px; text-align: left; font-size: 15px; line-height: 1.2; background: #FFF; border-bottom: solid 1px #ccc; margin-left: 15px; margin-right: 15px; } .author-block h3 { margin: 0 0 10px; color: #227cdc; } .author-block p { margin: 5px 0; } .author-b { display: flex; justify-content: space-between; flex-wrap: wrap; margin-bottom:10px; } .author-b .ainfo { flex: 1 1 30%; box-sizing: border-box; text-align: left; background: #dcdcdc; padding: 7px 14px; border-radius: 5px; margin-top: 3px; margin-right: 10px; } @media (max-width: 768px) { .author-b .ainfo { flex: 1 1 100%; margin: 10px 0; } } </style> <script> function displayResults_author(authors) { var resultsContainer = document.getElementById('results_author'); resultsContainer.innerHTML = ''; if (!authors || Object.keys(authors).length === 0) { resultsContainer.innerHTML = '<p>No authors found.</p>'; return; } Object.values(authors).slice(0, 10).forEach(author => { var authorBlock = document.createElement('div'); authorBlock.className = 'author-block'; var author_name=author.name; let key_replace = author_name.replace(/ /g, '-'); key_replace = key_replace.toLowerCase(); var authorHTML = ` <h3><a href="https://cancerscience.net/author/index/${key_replace}\/${author.aid}" target="_blank" title="${author.name}">${author.name}</a></h3> <div class="author-b"> <div class="ainfo"><strong>H-Index:</strong> ${author.hindex}</div> <div class="ainfo"><strong>Publication Count:</strong> ${author.paper_count}</div> <div class="ainfo"><strong>Citation Count:</strong> ${author.citation_count}</div> </div> <p><strong>Affiliation:</strong> ${author.affiliation}</p> `; authorBlock.innerHTML = authorHTML; resultsContainer.appendChild(authorBlock); }); } function displayKeywordAuthors(keywords) { var resultsContainer = document.getElementById('keyword-authors'); resultsContainer.innerHTML = ''; if (!keywords || keywords.length === 0) { resultsContainer.innerHTML = '<p>No data found.</p>'; return; } var keywordHTML = ''; keywords.forEach(key => { let key_replace = key.replace(/ /g, '-'); key_replace = key_replace.toLowerCase(); keywordHTML += `<a href="https://cancerscience.net/expert-search/index/${key_replace}" target="_blank" title="${key}">${key}</a>`; }); resultsContainer.innerHTML = keywordHTML; } // Call the function with the PHP data var authors_data = { "LwnrK4wBWBy50K-rqmuZ": { "aid": "LwnrK4wBWBy50K-rqmuZ", "name": "S. Yau", "citation_count": 56047, "hindex": 112, "paper_count": 1202, "affiliation": "", "email": "s.s.sahota@soton.ac.uk", "slug_tail": "s-yau" }, "gccpK4wBWBy50K-ruLYP": { "aid": "gccpK4wBWBy50K-ruLYP", "name": "R. McLendon", "citation_count": 60729, "hindex": 108, "paper_count": 463, "affiliation": "Duke University Medical Center, Durham, DUMC 3712, NC 27710, USA. ", "email": "Mclen001@mc.duke.edu", "slug_tail": "r-mclendon" }, "3TJ2K4wBWBy50K-rFhZN": { "aid": "3TJ2K4wBWBy50K-rFhZN", "name": "H. Friedman", "citation_count": 48331, "hindex": 107, "paper_count": 771, "affiliation": "Department of Neurosurgery, The Preston Robert Tisch Brain Tumor Center, Duke University Medical Center, DUMC Box 3624, Durham, NC, 27710, USA. ", "email": "henry.friedman@duke.edu", "slug_tail": "h-friedman" }, "bVJDK4wBWBy50K-rNE8v": { "aid": "bVJDK4wBWBy50K-rNE8v", "name": "M. Berger", "citation_count": 55137, "hindex": 107, "paper_count": 684, "affiliation": "Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA. ", "email": "bergerm1@mskcc.org", "slug_tail": "m-berger" }, "9vYyK4wBWBy50K-rbXlE": { "aid": "9vYyK4wBWBy50K-rbXlE", "name": "J. Feijen", "citation_count": 41922, "hindex": 104, "paper_count": 1014, "affiliation": "Biomedical Polymers Laboratory, Jiangsu Key Laboratory of Advanced Functional Polymer Design and Application, College of Chemistry, Chemical Engineering and Materials Science, and State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou 215123, PR China. ", "email": "j.feijen@utwente.nl", "slug_tail": "j-feijen" }, "BrvZK4wBWBy50K-rDvYc": { "aid": "BrvZK4wBWBy50K-rDvYc", "name": "S. Leh\u00e9ricy", "citation_count": 31837, "hindex": 98, "paper_count": 516, "affiliation": "Institut Du Cerveau et de La Moelle \u00e9pini\u00e8re, Centre for NeuroImaging Research, Team Movement Investigation and Therapeutics, Sorbonne Universit\u00e9, UPMC - Inserm U1127, CNRS UMR, 7225, Paris, France. ", "email": "stephane.lehericy@upmc.fr", "slug_tail": "s-lehericy" }, "hHHmKowBWBy50K-rWC2F": { "aid": "hHHmKowBWBy50K-rWC2F", "name": "R. Jenkins", "citation_count": 42298, "hindex": 93, "paper_count": 598, "affiliation": "Division of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA. ", "email": "rjenkins@mayo.edu", "slug_tail": "r-jenkins" }, "42ISK4wBWBy50K-r-do6": { "aid": "42ISK4wBWBy50K-r-do6", "name": "M. J. van den Bent", "citation_count": 58003, "hindex": 92, "paper_count": 489, "affiliation": "Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands. 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Khalili", "citation_count": 24087, "hindex": 82, "paper_count": 590, "affiliation": "Department of Neuroscience Center for Neurovirology, Lewis Katz School of Medicine at Temple University, 3500N. Broad Street, Philadelphia, PA, 19140, USA. ", "email": "kamel.khalili@temple.edu", "slug_tail": "k-khalili" }, "gAU1K4wBWBy50K-rpDVr": { "aid": "gAU1K4wBWBy50K-rpDVr", "name": "R. Ehman", "citation_count": 25455, "hindex": 80, "paper_count": 494, "affiliation": "Mayo Clinic Minnesota Department of Radiology, 198511, Rochester, Minnesota, United States; ", "email": "Ehman.Richard@mayo.edu", "slug_tail": "r-ehman" }, "AhzuK4wBWBy50K-r_wu2": { "aid": "AhzuK4wBWBy50K-r_wu2", "name": "E. Neuwelt", "citation_count": 23086, "hindex": 78, "paper_count": 481, "affiliation": "Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, L603, Portland, OR, 97239, USA. 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Maldjian", "citation_count": 19186, "hindex": 62, "paper_count": 258, "affiliation": "From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas ", "email": "joseph.maldjian@utsouthwestern.edu", "slug_tail": "j-maldjian" }, "9zS5K4wBWBy50K-r6FUa": { "aid": "9zS5K4wBWBy50K-r6FUa", "name": "V. Ramaswamy", "citation_count": 17073, "hindex": 62, "paper_count": 405, "affiliation": "Division of Haematology\/Oncology, Department of Paediatrics, Hospital for Sick Children and University of Toronto, 555 University Ave, Toronto, ON, M5G 1X8, Canada. ", "email": "vijay.ramaswamy@sickkids.ca", "slug_tail": "v-ramaswamy" } }; //console.log(authors_data); displayResults_author(authors_data); var keywordsArray = ["Brain Tumor Segmentation","Deep Learning","Machine Learning","Atlas-based Segmentation","Multi-modal Imaging","Probabilistic Models","4D Segmentation","MRI","Glioblastoma","Automated Segmentation"]; displayKeywordAuthors(keywordsArray); </script></p> </div> <footer class="entry-meta" aria-label="Entry meta"> <span class="cat-links"><span class="gp-icon icon-categories"><svg viewBox="0 0 512 512" aria-hidden="true" xmlns="http://www.w3.org/2000/svg" width="1em" height="1em"><path d="M0 112c0-26.51 21.49-48 48-48h110.014a48 48 0 0143.592 27.907l12.349 26.791A16 16 0 00228.486 128H464c26.51 0 48 21.49 48 48v224c0 26.51-21.49 48-48 48H48c-26.51 0-48-21.49-48-48V112z" /></svg></span><span class="screen-reader-text">Categories </span><a href="https://cancerscience.net/archive/category/brain-tumor/" rel="category tag">Brain 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