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<!DOCTYPE html>
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    <title>LDTM-MICCAI2024</title>
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<body>
    <header>
        <!-- <img src="Djemaa_el_Fna.jpg" alt="Conference Image" width="300" height="150"> -->
        <h1 style="color:#343a40; text-align: center">Longitudinal Disease Tracking and Modelling with Medical Images
            and Data (LDTM)</h1>
        <h2 style="color: #7B1D09; text-align: center">A Satellite Workshop with <img src="assets/miccai2023-logo.png" ,
                width="250" , height="130"></h2>
        <h1 style="color:#7B1D09"> October 10, 2024</h1>
    </header>
    <nav class="d-none d-md-block">
        <a href="#aims-and-scope">Aims and Scope</a>
        <a href="#call-for-papers">Call for Papers</a>
        <a href="#important-dates">Important Dates</a>
        <a href="#agenda">Agenda</a>
        <a href="#organizers">Organizers</a>
    </nav>
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            Menu
        </button>
        <div class="collapse" id="mobileMenu">
            <div class="card card-body">
                <a href="#aims-and-scope">Aims and Scope</a>
                <a href="#call-for-papers">Call for Papers</a>
                <a href="#important-dates">Important Dates</a>
                <a href="#agenda">Agenda</a>
                <a href="#organizers">Organizers</a>
            </div>
        </div>
    </div>
    <div class="content">
        <section id="aims-and-scope">
            <h2>Aims and Scope</h2>
            <p>Serial imaging is an integral part of the clinical workflow and is routinely employed to track the
                progression of disease or to assess response to treatment. For example, patients with cancer are often
                followed up longitudinally using radiological imaging (e.g., CT/MR/PET) to identify and track lesions,
                and assess treatment response.

                Recent developments in AI and machine learning have shown promise in automating or improving parts of
                the clinical workflow, from being able to find lesions in various organs, to classification and
                diagnosis of diseases. Unfortunately, despite the key role of serial imaging in the clinical workflow,
                developing AI systems that can track or model disease progression by learning from and exploiting
                longitudinal imaging has not received much attention until recently. Besides their role in the everyday
                clinical workflow, such models can enhance biological understanding of diseases to shape prevention and
                intervention strategies, inform clinical trial design, and support clinical decision making, such as
                patient diagnosis and prognosis.Therefore, in this workshop, we solicit submissions which adhere to the
                general theme of tracking and modeling disease progression with imaging and/or multimodal data.</p>

            <p>Two areas are of particular interest:</p>
            <ul>
                <li>Area 1: Cancer and concomitant diseases, where possible themes include tasks, such as detection,
                    classification or segmentation of lesions or other abnormalities with or without multiple time
                    points,
                    combining imaging with other multi-modal data (e.g. radiology reports, electronic medical records),
                    combining imaging and large language models (LLMs) to identify and track findings and disease status
                    over time as well as mechanistic models.</li>
                <li>Area 2: Neurodegenerative diseases, where data driven disease progression modeling (DPM), often
                    using
                    imaging biomarkers, has received increasing interest recently. In our recent publications in Nature
                    Reviews Neuroscience, we provide comprehensive overviews of the current state-of-the-art
                    phenomenological and pathophysiological models. Phenomenological models simultaneously estimate
                    the
                    disease progression trajectories from observed data together with the corresponding temporal axis
                    and
                    have important applications in patient subtyping and staging. Pathophysiological models aim to
                    understand underlying disease mechanisms by modeling connectome-moderated pathology appearance and
                    spreading, brain networks, and other dynamical systems models. Key emerging topics include:
                    integrating
                    pathophysiological and phenomenological models, pathophysiological models of the interaction of
                    multiple
                    disease mechanisms, and the role of deep learning in DPM.</li>
            </ul>
            <p>The workshop is not limited to the above disease areas and we welcome submissions related to other
                diseases or applications as well. While the focus is on longitudinal data, we also welcome submissions
                which perform DPM with cross-sectional data. Finally, we also welcome and encourage submissions related
                to new datasets that can enable the research of tomorrow.</p>

            <p>The scope of the conference includes, but is not limited to:</p>
            <ul>
                <li>Detection, classification, segmentation of abnormal anatomical structures. Areas of interest
                    include, but are not limited to the following: lesions, tumors, lymph nodes, neurodegenerative
                    changes etc.
                </li>
                <li>Disease progression modeling</li>
                <li>Longitudinal data analysis</li>
                <li>Follow-up assessment</li>
                <li>Disease tracking and outcome prediction with longitudinal and/or multi-modal data</li>
                <li>Surgical and Computer-Assisted interventions to determine/track changes</li>
                <li>Histopathological analysis and change tracking over time</li>
                <li>Image registration of longitudinal series for subsequent assessment</li>
                <li>Multi-modal data fusion for detection, diagnosis, and image-guided interventions</li>
                <li>New datasets and metrics</li>
                <!-- Add more topics as needed -->
            </ul>
        </section>
        <section id="call-for-papers">
            <h2>Call for Papers</h2>
            <p>We solicit submissions consistent with the aims and scope of the workshop. The following types of
                submissions will be accepted:
            <ul>
                <li><strong>Full Papers:</strong> We encourage the submissions of full-length papers (<strong>upto 10 pages
                        excluding references</strong>) describing new work that has not been previously published or
                    accepted for publication. Accepted papers will be presented as orals and/or posters.</li>
                <li><strong>Extended Abstracts:</strong> We encourage the submissions of short papers (<strong> upto 3 pages +
                        1 page references</strong>) describing new, previously, or concurrently published research or
                    work-in-progress. The paper will be presented as a poster.</li>
                <li><strong>Accepted manuscripts from MICCAI main conference:</strong> We also invite authors of an
                    accepted manuscript at MICCAI which is of interest for our workshop to present their work in an oral
                    presentation at our workshop to strengthen the community around this topic.</li>
            </ul>
            All full papers and extended abstracts must adhere to the formatting and style used by the MICCAI main
            conference -- they should be anonymized and use the manuscript templates available at <a
                href="https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines">Lecture Notes
                in Computer Science</a>. Accepted <strong>full papers</strong> will be published as a Springer Lecture Notes in Computer Science (LNCS) series.
            </p>
        </section>

        <section id="important-dates">
            <h2>Important Dates</h2>
            All deadlines are <strong>23:59 UTC-12/Anywhere on Earth (AoE)</strong>
            <ul>
                <li><strong>Full paper submission deadline: June 24, 2024</strong></li>
                <li>Notification of acceptance: July 15, 2024</li>
                <li>Camera Ready paper submission: August 5, 2024</li>
                <li>Extended Abstracts submission: August 5, 2024</li>
                <li>Notification of acceptance (abstracts): August 19, 2024</li>
            </ul>
        </section>

        <section id="agenda">
            <h2>Agenda</h2>
            <p>The agenda for the conference will be updated soon.</p>
        </section>


        <section id="organizers">
            <h2>Organizers</h2>
            <div class="grid">
                <div class="organizer">
                    <img src="assets/Alessa_Hering.jpg" alt="Alessa Hering">
                    <div class="organizer-info">
                        <div class="organizer-name">Alessa Hering</div>
                        <div class="organizer-affiliation">Radboudumc, Nijmegen, The Netherlands <br> and Fraunhofer
                            MEVIS, Germany</div>
                    </div>
                </div>

                <div class="organizer">
                    <img src="assets/alexandra_young.jpg" alt="Alexandra Young">
                    <div class="organizer-info">
                        <div class="organizer-name">Alexandra Young</div>
                        <div class="organizer-affiliation">Centre for Medical Image Computing, <br> University College
                            London, London, UK</div>
                    </div>
                </div>

                <div class="organizer">
                    <img src="assets/anna_schroder.jpg" alt="Anna Schroder">
                    <div class="organizer-info">
                        <div class="organizer-name">Anna Schroder</div>
                        <div class="organizer-affiliation">Centre for Medical Image Computing, <br> University College
                            London, London, UK</div>
                    </div>
                </div>

                <div class="organizer">
                    <img src="assets/bruno_jedynak.jpg" alt="Bruno Jedynak">
                    <div class="organizer-info">
                        <div class="organizer-name">Bruno Jedynak</div>
                        <div class="organizer-affiliation">Department of Mathematics and Statistics, <br> Portland State
                            University, Portland, USA</div>
                    </div>
                </div>

                <div class="organizer">
                    <img src="assets/jacob_vogel.jpg" alt="Jacob Vogel">
                    <div class="organizer-info">
                        <div class="organizer-name">Jacob Vogel</div>
                        <div class="organizer-affiliation">Clinical Memory Research Unite, <br> Lund University, Lund,
                            Sweden</div>
                    </div>
                </div>

                <div class="organizer">
                    <img src="assets/neil_oxtoby.jpg" alt="Neil Oxtoby">
                    <div class="organizer-info">
                        <div class="organizer-name">Neil Oxtoby</div>
                        <div class="organizer-affiliation">Centre for Medical Image Computing, <br> University College
                            London, London, UK</div>
                    </div>
                </div>

                <div class="organizer">
                    <img src="assets/peter_wijeratne.jpg" alt="Peter Wijeratne">
                    <div class="organizer-info">
                        <div class="organizer-name">Peter Wijeratne</div>
                        <div class="organizer-affiliation">Department of Informatics, <br> University of Sussex,
                            Brighton, UK</div>
                    </div>
                </div>

                <div class="organizer">
                    <img src="assets/pritam_mukherjee.jpg" alt="Pritam Mukherjee">
                    <div class="organizer-info">
                        <div class="organizer-name">Pritam Mukherjee</div>
                        <div class="organizer-affiliation">National Institutes of Health, <br> Clinical Center,
                            Bethesda, USA</div>
                    </div>
                </div>

                <div class="organizer">
                    <img src="assets/sara_garbarino.jpg" alt="Sara Garbarino">
                    <div class="organizer-info">
                        <div class="organizer-name">Sara Garbarino</div>
                        <div class="organizer-affiliation">Life Science Computational Laboratory, <br> IRCCS Ospedale
                            Policlinico San Martino, Genoa, Italy</div>
                    </div>
                </div>

                <div class="organizer">
                    <img src="assets/sven_kuckertz.jpg" alt="Sven Kuckertz">
                    <div class="organizer-info">
                        <div class="organizer-name">Sven Kuckertz</div>
                        <div class="organizer-affiliation">Fraunhofer MEVIS, <br> Lübeck, Germany,</div>
                    </div>
                </div>

                <div class="organizer">
                    <img src="assets/tejas_mathai.jpg" alt="Tejas Mathai">
                    <div class="organizer-info">
                        <div class="organizer-name">Tejas Mathai</div>
                        <div class="organizer-affiliation">National Institutes of Health, <br> Clinical Center,
                            Bethesda, USA</div>
                    </div>
                </div>

                <div class="organizer">
                    <img src="assets/tiantian_he.jpg" alt="Tiantian He">
                    <div class="organizer-info">
                        <div class="organizer-name">Tiantian He</div>
                        <div class="organizer-affiliation">Centre for Medical Image Computing, <br> University College
                            London, London, UK</div>
                    </div>
                </div>


            </div>
            <!-- Add more organizers as needed -->
        </section>


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