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            <h1 class="section-title">Abstract</h1>
            <ul class="news-list">
                Radiotherapy is a critical cancer treatment that uses external beam radiation to kill cancer cells.
                Effective treatment planning, which determines radiation dose distribution for tumor volumes, is
                essential to maximize
                the likelihood of a cure while minimizing toxicity. Gross Tumor Volume (GTV) and Clinical Target
                Volume (CTV) are two
                key targets in radiotherapy planning. GTV is the visible tumor extent on Computed Tomography (CT)
                scans, while CTV
                encompasses GTV and describes the extent of microscopic, unimageable tumor spread. Accurate
                delineation of GTV and CTV
                is essential in radiotherapy planning, however, manual slice-by-slice annotation on CT scans is
                time-consuming and
                labor-intensive for radiation oncologists. Automating this process can significantly reduce planning
                time and enhance
                radiotherapy efficiency. <br>
                Based on the success of SegRap2023, SegRap2025 aims to address ongoing challenges in GTV
                segmentation and firstly focus
                on the Lymph Node (LN) CTV segmentation. It provides two datasets collected from diverse cohorts:
                one with data from 260
                NPC patients annotated for GTV, and another with 402 patients annotated for LN CTV. It also provides
                an unlabeled
                dataset of CT scans from 500 patients to support model training. Based on the extensive and
                comprehensive datasets, two
                sub-tasks will be held in SegRap2025:

                <li><strong>Task01</strong>: GTV Segmentation</li>
                <li><strong>Task02</strong>: LN CTV Segmentation</li>

                We believe that these algorithms will have the potential to support clinical manual delineation, enhance
                research on
                radiation dose calculation, and improve efficiency of radiotherapy treatment planning.

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            <h1 class="section-title">Important Dates</h1>
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                <li><strong>Registration opens</strong>: May 1st (12:00 AM GMT), 2025</li>
                <li><strong>Release of training and validation data</strong>: May 10th (12:00 AM GMT), 2025</li>
                <li><strong>Validation results evaluation</strong>: June 30th ~ Aug. 10th (12:00 AM GMT), 2025</li>
                <li><strong>Docker and short paper submission</strong>: Aug. 10th ~ Aug. 31st (12:00 AM
                    GMT), 2025</li>
                <li><strong>Announcement of final results</strong>: Sep. 23th, 2025</li>
            </ul>
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            <h1 class="section-title">References</h1>
            <ul class="news-list">
                You can find our previous challenges here:
                <a href="https://segrap2023.grand-challenge.org/"><strong>SegRap2023</strong></a>, associated paper with
                summary of
                the results can be downloaded <a
                    href="https://doi.org/10.1016/j.media.2024.103447"><strong>here</strong></a>, and
                algorithms from top
                teams can be found at: <a
                    href="https://drive.google.com/file/d/1uI7idYOF87G6Bjx1tes_VSteXpbrT3kD/view"><strong>Google-Drive</strong></a>.
                <br>
                <br>
                Please cite the following when using the SegRap dataset in your research:
                <li><a href="https://doi.org/10.1016/j.media.2024.103447">X. Luo et al. Segrap2023: A benchmark of
                        organs-at-risk and gross tumor volume segmentation for radiotherapy planning
                        of nasopharyngeal carcinoma. Medical image analysis 2025, 101: 103447.</a></li>
                <li><a href="https://doi.org/10.1038/s41597-024-03890-0">X. Luo et al. A multicenter dataset for lymph
                        node clinical target volume delineation of
                        nasopharyngeal carcinoma.
                        Scientific Data 11, 1085 (2024).</a></li>

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