RSNA-2024

RSNA 2024 Lumbar Spine Degenerative Classification 腰椎退行性分类

Classify lumbar spine degenerative conditions：对腰椎退行性疾病进行分类

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Overview 概述

The goal of this competition is to create models that can be used to aid in the detection and classification of degenerative spine conditions using lumbar spine MR images. Competitors will develop models that simulate a radiologist’s performance in diagnosing spine conditions.

本次竞赛的目标是创建可用于帮助使用腰椎 MR 图像检测和分类脊柱退行性疾病的模型。参赛者将开发模型来模拟放射科医生诊断脊柱疾病的表现。

Description 描述

Low back pain is the leading cause of disability worldwide, according to the World Health Organization, affecting 619 million people in 2020. Most people experience low back pain at some point in their lives, with the frequency increasing with age. Pain and restricted mobility are often symptoms of spondylosis, a set of degenerative spine conditions including degeneration of intervertebral discs and subsequent narrowing of the spinal canal (spinal stenosis), subarticular recesses, or neural foramen with associated compression or irritations of the nerves in the low back.

根据世界卫生组织的数据，腰痛是全球范围内导致残疾的主要原因，到 2020 年，腰痛将影响 6.19 亿人。大多数人在一生中的某个阶段都会经历腰痛，且频率随着年龄的增长而增加。疼痛和活动受限通常是脊椎病的症状，这是一组退行性脊柱疾病，包括椎间盘退变和随后的椎管变窄（椎管狭窄）、关节下隐窝或神经孔，并伴有下肢神经受压或刺激。

Magnetic resonance imaging (MRI) provides a detailed view of the lumbar spine vertebra, discs and nerves, enabling radiologists to assess the presence and severity of these conditions. Proper diagnosis and grading of these conditions help guide treatment and potential surgery to help alleviate back pain and improve overall health and quality of life for patients.

磁共振成像 (MRI) 提供腰椎、椎间盘和神经的详细视图，使放射科医生能够评估这些病症的存在和严重程度。对这些病症的正确诊断和分级有助于指导治疗和潜在的手术，以帮助减轻背痛并改善患者的整体健康和生活质量。

RSNA has teamed with the American Society of Neuroradiology (ASNR) to conduct this competition exploring whether artificial intelligence can be used to aid in the detection and classification of degenerative spine conditions using lumbar spine MR images.

RSNA 与美国神经放射学会 (ASNR)合作举办了本次竞赛，探讨人工智能是否可以利用腰椎 MR 图像来帮助检测和分类脊柱退行性疾病。

The challenge will focus on the classification of five lumbar spine degenerative conditions: Left Neural Foraminal Narrowing, Right Neural Foraminal Narrowing, Left Subarticular Stenosis, Right Subarticular Stenosis, and Spinal Canal Stenosis. For each imaging study in the dataset, we’ve provided severity scores (Normal/Mild, Moderate, or Severe) for each of the five conditions across the intervertebral disc levels L1/L2, L2/L3, L3/L4, L4/L5, and L5/S1.

挑战将集中于五种腰椎退行性疾病的分类：左神经椎间孔狭窄、右神经椎间孔狭窄、左关节下狭窄、右关节下狭窄和椎管狭窄。对于数据集中的每项影像学研究，我们为椎间盘 L1/L2、L2/L3、L3/L4、L4/L5 级别的五种情况中的每一种提供了严重性评分（正常/轻度、中度或严重）和 L5/S1。

To create the ground truth dataset, the RSNA challenge planning task force collected imaging data sourced from eight sites on five continents. This multi-institutional, expertly curated dataset promises to improve standardized classification of degenerative lumbar spine conditions and enable development of tools to automate accurate and rapid disease classification.

为了创建实况数据集，RSNA 挑战计划工作组收集了来自五大洲八个站点的成像数据。这个多机构、专业策划的数据集有望改善退行性腰椎疾病的标准化分类，并支持开发自动化准确、快速的疾病分类工具。

Challenge winners will be recognized at an event during the RSNA 2024 annual meeting. For more information on the challenge, contact RSNA Informatics staff at informatics@rsna.org.

挑战赛获胜者将在 RSNA 2024 年年会期间的活动中获得表彰。有关挑战的更多信息，请联系 RSNA 信息学工作人员： informatics@rsna.org 。

Evaluation 评估

Submissions are evaluated using the average of sample weighted log losses and an any_severe_spinal prediction generated by the metric. The metric notebook can be found here.

使用样本加权对数损失的平均值和由该指标生成的any_severe_spinal预测来评估提交的结果。公制笔记本可以在这里找到。

The sample weights are as follows:

样本权重如下：

• 1 for normal/mild. 1 为正常/轻度。
• 2 for moderate. 2为中等。
• 4 for severe. 4为严重。

For each row ID in the test set, you must predict a probability for each of the different severity levels. The file should contain a header and have the following format:

对于测试集中的每个行 ID，您必须预测每个不同严重性级别的概率。该文件应包含标头并具有以下格式：

row_id,normal_mild,moderate,severe
123456_left_neural_foraminal_narrowing_l1_l2,0.333,0.333,0.333
123456_left_neural_foraminal_narrowing_l2_l3,0.333,0.333,0.333
123456_left_neural_foraminal_narrowing_l3_l4,0.333,0.333,0.333
etc.

row_id	normal_mild	moderate	severe
123456_left_neural_foraminal_narrowing_l1_l2	0.333	0.333	0.333
123456_left_neural_foraminal_narrowing_l2_l3	0.333	0.333	0.333
123456_left_neural_foraminal_narrowing_l3_l4	0.333	0.333	0.333
etc.

In rare cases the lowest vertebrae aren’t visible in the imagery. You still need to make predictions (nulls will cause errors), but those rows will not be scored.

在极少数情况下，图像中看不到最低的椎骨。您仍然需要进行预测（空值会导致错误），但这些行不会被评分。

For this competition, the any_severe_scalar has been set to 1.0.

对于本次比赛，any_severe_scalar已设置为1.0 。

Dataset Description 数据集描述

The goal of this competition is to identify medical conditions affecting the lumbar spine in MRI scans.
本次比赛的目标是通过 MRI 扫描识别影响腰椎的医疗状况。

This competition uses a hidden test. When your submitted notebook is scored, the actual test data (including a full length sample submission) will be made available to your notebook.
本次比赛采用隐藏测试方式。当您提交的笔记本电脑被评分时，实际的测试数据（包括完整长度的样本提交）将提供给您的笔记本电脑。

Files 文件

train.csv Labels for the train set. 训练集的标签。

• study_id - The study ID. Each study may include multiple series of images.
  study_id - 研究 ID，每个研究可能包括多个系列的图像。
• [condition]_[level] - The target labels, such as spinal_canal_stenosis_l1_l2 , with the severity levels of Normal/Mild, Moderate , or Severe . Some entries have incomplete labels.
  [condition]_[level] - 目标标签，例如spinal_canal_stenosis_l1_l2 ，严重程度级别为 Normal/Mild 、 Moderate 或 Severe。有些条目的标签不完整。

train_label_coordinates.csv
训练标签坐标.csv

• study_id
• series_id - The imagery series ID.
  series_id - 图像系列 ID。
• instance_number - The image’s order number within the 3D stack.
  instance_number - 图像在 3D 堆栈中的顺序号。
• condition - There are three core conditions: spinal canal stenosis, neural_foraminal_narrowing, and subarticular_stenosis. The latter two are considered for each side of the spine.
  condition - 共有三种核心病症：椎管狭窄、神经椎间孔狭窄和关节下狭窄。脊柱的每一侧都考虑后两者。
• level - The relevant vertebrae, such as l3_l4
level - 相关椎骨，例如l3_l4
• [x/y] - The x/y coordinates for the center of the area that defined the label.
  [x/y] - 定义标签的区域中心的 x/y 坐标。

sample_submission.csv 样本提交.csv

• row_id - A slug of the study ID, condition, and level such as 12345_spinal_canal_stenosis_l3_l4.
  row_id - 研究 ID、条件和级别的 slug，例如12345_spinal_canal_stenosis_l3_l4 。
• [normal_mild/moderate/severe] - The three prediction columns.
  [normal_mild/moderate/severe] - 三个预测列。

[train/test]_images/[study_id]/[series_id]/[instance_number].dcm The imagery data.图像数据。

[train/test]_series_descriptions.csv

• study_id

• series_id

• series_description The scan’s orientation.
  series_description 扫描方向。

方案说明

Transformer + KAN 6.09 结果很不好