[论文解读] The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)

Maruf Adewole, Jeffrey D. Rudie+49 more|arXiv (Cornell University)|May 30, 2023

AI in cancer detection被引用 34

一句话总结

本文提出 BraTS-Africa，一项 BraTS 2023 赛道，专注于撒哈拉以南非洲的胶质瘤分割，以在资源有限的条件下评估 CAD 方法。

ABSTRACT

Gliomas are the most common type of primary brain tumors. Although gliomas are relatively rare, they are among the deadliest types of cancer, with a survival rate of less than 2 years after diagnosis. Gliomas are challenging to diagnose, hard to treat and inherently resistant to conventional therapy. Years of extensive research to improve diagnosis and treatment of gliomas have decreased mortality rates across the Global North, while chances of survival among individuals in low- and middle-income countries (LMICs) remain unchanged and are significantly worse in Sub-Saharan Africa (SSA) populations. Long-term survival with glioma is associated with the identification of appropriate pathological features on brain MRI and confirmation by histopathology. Since 2012, the Brain Tumor Segmentation (BraTS) Challenge have evaluated state-of-the-art machine learning methods to detect, characterize, and classify gliomas. However, it is unclear if the state-of-the-art methods can be widely implemented in SSA given the extensive use of lower-quality MRI technology, which produces poor image contrast and resolution and more importantly, the propensity for late presentation of disease at advanced stages as well as the unique characteristics of gliomas in SSA (i.e., suspected higher rates of gliomatosis cerebri). Thus, the BraTS-Africa Challenge provides a unique opportunity to include brain MRI glioma cases from SSA in global efforts through the BraTS Challenge to develop and evaluate computer-aided-diagnostic (CAD) methods for the detection and characterization of glioma in resource-limited settings, where the potential for CAD tools to transform healthcare are more likely.

研究动机与目标

• Motivate evaluation of state-of-the-art glioma segmentation methods in Sub-Saharan Africa using BraTS framework.
• Assess how SSA-specific factors (MRI quality, presentation, and tumor characteristics) affect segmentation performance.
• Promote development of computer-aided-diagnostic (CAD) tools suitable for resource-limited healthcare settings.

提出的方法

• Extend BraTS framework to include SSA glioma cases.
• Encourage participation of SSA data contributors to develop and test CAD methods.
• Focus on detection, characterization, and segmentation of gliomas under varied SSA imaging conditions.
• Highlight potential for CAD tools to transform brain tumor care in LMICs.

实验结果

研究问题

• RQ1 Can state-of-the-art BraTS segmentation methods generalize to Sub-Saharan Africa MRI data with lower image quality?
• RQ2 What SSA-specific tumor characteristics and imaging limitations impact glioma segmentation performance?
• RQ3 Do CAD tools have the potential to improve diagnosis and treatment planning in resource-limited SSA settings?

主要发现

• BraTS-Africa provides a unique opportunity to include SSA glioma cases in global BraTS efforts.
• The challenge aims to develop and evaluate CAD methods for detection and characterization of glioma in settings with limited resources.
• SSA-specific imaging quality and presentation patterns are considered as part of the evaluation framework.
• The study emphasizes potential improvements in healthcare through CAD tools tailored for SSA conditions.