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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: 1%;
margin-bottom: 20px;
line-height: 1.3;
color: #000000;
font-size: 0.95em;
font-weight: 400;
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.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">Tasks</a>
<a href="dataset.html" class="active">Dataset</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">Description</h1>
<div class="metric-name">Task01: GTV segmentation</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 two cohorts: <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 class was also stored individually. The expected output from your algorithm
should be a set of label maps.
</p>
<br>
<div class="metric-name">Task02: LN CTV Segmentation</div>
<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 108 ceCT) with
a corresponding label map, as well as <strong>500 unlabeled cases</strong>.</li>
<li>Validation data will consist of <strong>40 patients from external testing
cohort</strong>: <em>20 with paired CT</em>, <em>10 with only ncCT</em>, and <em>10 with
only ceCT</em>.
</li>
<li>Testing data will consist of <strong>100 patients from external testing
cohort</strong>: <em>40 with paired CT</em>, <em>30 with only ncCT</em>, and <em>30 with
only ceCT</em>.</li>
</ul>
<!-- <br> -->
<p class="metric-description">
<em>Note:</em> All LN CTVs were annotated individually by oncologists using ITKSNAP. The expected output
from your algorithm should be a set of label maps.
</p>
<br>
<h2 class="section-title">Download</h2>
<div class="task-content">
<div class="metric-name">Registration</div>
<p class="metric-description">
Please fill out the <a
href="https://docs.google.com/forms/d/e/1FAIpQLSelLeqBJ7kaFT4QzBhW85ze6EDUvWCB3ig_Mm2yp6HiLdJbpg/viewform?usp=header">Registration
form</a>.
</p>
<br>
<div class="metric-name">Task01: GTV Segmentation</div>
<p class="metric-description">
The training data (with labels) and validation data (without lables) can be downloaded at: <a
href="https://drive.google.com/drive/folders/115mzmNlZRIewnSR2QFDwW_-RkNM0LC9D">GoogleDrive</a>
and <a
href="https://pan.baidu.com/s/1KYH4j5CQO_qx7wg7GkkR7Q?pwd=2023#list/path=%2F">BaiduNetDisk</a>.
The unzip password is <em>segrap2023@uestc</em>.
</p>
<br>
<div class="metric-name">Task02: LN CTV Segmentation</div>
<p class="metric-description">
The training data (with labels) can be downloaded at: <a
href="https://figshare.com/articles/dataset/LNCTVSeg-DataSet_zip/26793622?file=48684664">here</a>,
and the unzip passowrd is <em>lnctvseg@uestc</em>.
<br>
The validation data (without labels) can be
downloaded at: <a
href="https://drive.google.com/file/d/1vcEX4aLnwi32c10ronFbdxMy49JDhjtQ/view?usp=sharing">GoogleDrive</a>
and <a href="https://pan.baidu.com/s/18ZKqRBOWR0BWFQ9Z6HXv2w?pwd=2025">BaiduNetDisk</a>.
<br>
</p>
<br>
<div class="metric-name">Supplementary unlabeled data</div>
<p class="metric-description">
A total of 500 <strong>unlabeled images</strong> are provided at: <a
href="https://drive.google.com/file/d/1pfYGXHg62gV-77LYv-U-9_hdQh81KYHb/view?usp=sharing">GoogleDrive</a>
and <a href="https://pan.baidu.com/s/1JzayTGV-EBuXYeiLhGlfew?pwd=2025">BaiduNetDisk</a>.
Participants
may explore
self-supervised or semi-supervised learning strategies to enhance model generalizability.
</p>
<br>
<div class="metric-name">Note</div>
<ul class="news-list">
<li>Please fill out the <a
href="https://drive.google.com/file/d/1KvEB41N4PvYyAAha--xDrCbvUEWg9Ljm/view?usp=sharing"><strong>EndUserAgreement</strong></a>,
and email a scan of the signed document to <em><strong>segrap2025@163.com</strong></em>. After
receiving online <em>Registration form</em> and <em>EndUserAgreement</em>, we will provide
you with the unzip password for the Task02 validation set and Supplementary unlabeled data.
</li>
<li>SegRap2025 focuses on the GTV and LN CTV segmentation. Participants are encouraged to leverage
<strong>OAR anatomical information</strong> to support GTV segmentation, but segmentation of
OARs are not necessary.
</li>
<li>The use of <strong>foundation models</strong> is permitted, but additional external data are not
allowed. Only the official data can be used.
</li>
</ul>
<br>
</div>
</section>
</main>
</body>
</html>