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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>SegRap2025 Challenge</title>
<link rel="stylesheet" href="shared.css">
<style>
.task-section {
margin-bottom: 60px;
}
.task-content {
background: white;
border-radius: 12px;
padding: 20px;
margin-bottom: 20px;
}
.task-content li {
color: #064b43;
margin-bottom: 0.3em;
line-height: 1.5;
}
.task-overview {
margin-bottom: 40px;
line-height: 1.6;
}
.evaluation-metrics {
margin-top: 20px;
}
.metric-item {
margin-bottom: 20px;
}
.metric-name {
font-weight: 500;
color: var(--text-primary);
font-size: 20px;
margin-bottom: 8px;
margin-top: 0px;
}
.metric-description {
color: var(--text-secondary);
font-size: 0.95em;
line-height: 1.5;
}
.news-list {
list-style: none;
padding: 0;
margin: 0;
}
.news-list li {
position: relative;
padding-left: 25px;
margin-top: 20px;
margin-bottom: 20px;
line-height: 1.5;
color: #064b43;
font-weight: 500;
}
.news-list li::before {
content: "•";
position: absolute;
left: 10px;
color: #00bfa5;
}
.news-list a {
color: #00bfa5;
text-decoration: none;
font-weight: 500;
transition: color 0.2s ease;
}
.news-list a:hover {
color: #00897b;
}
</style>
</head>
<body>
<div class="site-header">
<div class="header-image" style="text-align: center;">
<img src="logo_web.png" alt="SegRap Logo" style="width: 50%; height: auto;">
</div>
<div class="header-container">
<div class="content-section">
<nav class="nav-links">
<a href="index.html">Home</a>
<a href="tasks.html" class="active">Tasks</a>
<a href="evaluate.html">Evaluate</a>
<a href="prizes.html">Prizes</a>
<a href="leaderboard.html">Leaderboard</a>
<a href="organizing.html">Organizing</a>
<a href="contact.html">Contact</a>
</nav>
</div>
</div>
</div>
<main class="main-content">
<!-- Overview Section -->
<section id="overview" class="task-section">
<h1 class="section-title">Challenge Tasks</h1>
<div class="task-content">
<p>The SegRap2025 Challenge focuses on accurate Gross Tumor Volume (GTV) and Lymph Node Clinical Target
Volume (LN CTV) segmentation in Computed Tomography (CT) images, aiming to support clinical manual
delineation, enhance research on radiation dose calculation, and improve efficiency of
radiotherapy treatment planning.
</p>
</div>
<!-- Task01 -->
<h2 class="section-title">Task01: GTV Segmentation</h2>
<div class="task-content">
<div class="metric-name">Task Description</div>
<p class="metric-description">
Accurately delineate Gross Target Volume of nasopharynx (GTVnx, also named GTVp) and Gross
Target Volume of lymph node (GTVnd) within paired non-contrast computed tomography
(ncCT) and contrast computed tomography (ceCT) images.
</p>
<br>
<div class="metric-name">Dataset Description</div>
<p class="metric-description">
SegRap2025 Dataset will consist of CT images collected by Siemens CT scanners with the following
scanning conditions:
bulb voltage, 120 kV; current, 300 mA; scan thickness, 3.0 mm; resolution, 1024 × 1024 or 512
× 512; injected contrast
agent, iohexol (volume, 60~80 mL; rate, 2 mL/s; without delay). The dataset consists of clinically
required non-contrast
CT images (ncCT) and contrast CT images (ceCT) from patients with nasopharyngeal cancer before
treatment.
</p>
<br>
<p class="metric-description">
The dataset consists of clinically required <strong>non-contrast CT images (ncCT)</strong> and
<strong>contrast CT images (ceCT)</strong> from patients with nasopharyngeal cancer before
treatment.
</p>
<ul class="news-list">
<li>Training data will consist of CT images from <strong>120 patients</strong> with a
corresponding
label map, as well as <strong>500 unlabeled cases</strong>.</li>
<li>Validation data will consist of CT images from <strong>20 patients</strong>.</li>
<li>Testing data will consist of CT images from <strong>60 patients from internal testing
cohort</strong> and
<strong>60 patients from external testing cohort</strong>.
</li>
</ul>
<br>
<p class="metric-description">
<em>Note:</em> All GTVs were annotated individually by oncologists using MIM Software and ITKSNAP,
the
annotation of each organ
was also stored individually. The expected output from your algorithm should be a set of label
maps.
</p>
<br>
<div class="metric-name">Dataset Download</div>
<p class="metric-description">
Coming soon!
<!-- The training set with labels and the validation set without labels can be downloaded without any
additional requirements,
<a href="https://drive.google.com/drive/folders/115mzmNlZRIewnSR2QFDwW_-RkNM0LC9D">Google-Drive</a>,
<a href="https://pan.baidu.com/share/init?surl=KYH4j5CQO_qx7wg7GkkR7Q&pwd=2023">BaiduYun</a>, the
unzipped
password is <em>segrap2023@uestc</em>. -->
</p>
</div>
<!-- Task02 -->
<h2 class="section-title">Task02: LN CTV Segmentation</h2>
<div class="metric-name">Task Description</div>
<!-- <h3 class="task-description">Task Description</h3> -->
<p class="metric-description">
Accurately delineate 6 LN CTVs: L_Ib, L_II+III+Va, L_IV+Vb+Vc, R_Ib, R_II+III+Va, and R_IV+Vb+Vc
within paired ncCT and ceCT images or ncCT/ceCT images.
</p>
<br>
<div class="metric-name">Dataset Description</div>
<!-- <h3 class="data-description">Dataset Description</h3> -->
<p class="metric-description">
SegRap2025 Dataset will consists of CT images from Sichuan Cancer Hospital are collected by a
Brilliance CT Big Bore
system from Philips Healthcare (Philips Healthcare, Best, the Netherlands), with the following
scanning conditions: bulb
voltage at 120 kV, current ranging from 275 to 375 mA, slice thickness of 3.0 mm, and full
resolution of 512 × 512. An
injected contrast agent, iohexol, was used during the ceCT examination. Similarly, CT images from
Sichuan Provincial
People's Hospital, The First Affiliated Hospital of University of Science and Technology of China
and Southern Medical
University were acquired using a Somatom Definition AS 40 system from Siemens Healthcare (Siemens
Healthcare, Forcheim,
Germany), with the following conditions: bulb voltage ranging from 120 to140 kV, current ranging
from 280 to 380 mA,
slice thickness of 3.0 mm, and full resolution of 512 × 512. CT images from Daguan Hospital of
Chengdu Jinjiang were
acquired using a Somatom Definition AS 40 system from Siemens Healthcare (Siemens Healthcare,
Forcheim, Germany), with
the following conditions: bulb voltage 120 kV, current ranging from 200 to 250 mA, slice thickness
of 2.5 mm, and full
resolution of 512 × 512.
</p>
<br>
<p class="metric-description">
The dataset consists of clinically required <strong>non-contrast CT images (ncCT)</strong> and/or
<strong>contrast CT images (ceCT)</strong> from patients with nasopharyngeal cancer before
treatment.
</p>
<ul class="news-list">
<li>Training data will consist of CT images from <strong>262 patients from five cohorts</strong>
(150 paired CT, 32 ncCT and 90 ceCT) with
a corresponding label map, as well as <strong>500 unlabeled cases</strong>.</li>
<li>Validation data will consist of CT images from <strong>40 patients from external testing
cohort</strong> (20 paired CT, 10 ncCT and 10
ceCT).</li>
<li>Testing data will consist of CT images from <strong>100 patients from external testing
cohort</strong> (40 paired CT, 30 ncCT and 30
ceCT).</li>
</ul>
<br>
<p class="metric-description">
<em>Note:</em> All LN CTVs were annotated individually by oncologists using MIM Software and
ITKSNAP, the annotation of each organ was
also stored individually. The expected output from your algorithm should be a set of label maps.
</p>
<br>
<div class="metric-name">Dataset Download</div>
<!-- <h3 class="data-description">Dataset Download</h3> -->
<p class="metric-description">
Coming soon!
<!-- The training set with labels can be downloaded without any additional requirements at:
<a
href="https://figshare.com/articles/dataset/LNCTVSeg-DataSet_zip/26793622?file=48684664">LNCTVSeg</a>,
the unzipped password is <em>lnctvseg@uestc</em>. -->
</p>
</section>
</main>
</body>
</html>