[论文解读] Brain Cancer Segmentation Using YOLOv5 Deep Neural Network

Sudipto Paul, Md Taimur Ahad+1 more|arXiv (Cornell University)|Dec 27, 2022

Brain Tumor Detection and Classification被引用 11

一句话总结

本文提出了一种基于YOLOv5的实例分割框架，用于在MRI扫描中自动检测脑肿瘤，可对三种肿瘤类型——脑膜瘤、垂体瘤和胶质瘤——进行分类。该模型在10核GPU上实现了高效推理，且精度具有竞争力，证明了YOLOv5在早期诊断中用于医学图像分割的有效性。

ABSTRACT

An expansion of aberrant brain cells is referred to as a brain tumor. The brain's architecture is extremely intricate, with several regions controlling various nervous system processes. Any portion of the brain or skull can develop a brain tumor, including the brain's protective coating, the base of the skull, the brainstem, the sinuses, the nasal cavity, and many other places. Over the past ten years, numerous developments in the field of computer-aided brain tumor diagnosis have been made. Recently, instance segmentation has attracted a lot of interest in numerous computer vision applications. It seeks to assign various IDs to various scene objects, even if they are members of the same class. Typically, a two-stage pipeline is used to perform instance segmentation. This study shows brain cancer segmentation using YOLOv5. Yolo takes dataset as picture format and corresponding text file. You Only Look Once (YOLO) is a viral and widely used algorithm. YOLO is famous for its object recognition properties. You Only Look Once (YOLO) is a popular algorithm that has gone viral. YOLO is well known for its ability to identify objects. YOLO V2, V3, V4, and V5 are some of the YOLO latest versions that experts have published in recent years. Early brain tumor detection is one of the most important jobs that neurologists and radiologists have. However, it can be difficult and error-prone to manually identify and segment brain tumors from Magnetic Resonance Imaging (MRI) data. For making an early diagnosis of the condition, an automated brain tumor detection system is necessary. The model of the research paper has three classes. They are respectively Meningioma, Pituitary, Glioma. The results show that, our model achieves competitive accuracy, in terms of runtime usage of M2 10 core GPU.

研究动机与目标

• 开发一种基于深度学习的自动化系统，用于早期检测MRI扫描中的脑肿瘤。
• 解决人工分割所面临的耗时且易出错的挑战。
• 应用YOLOv5——一种实时目标检测模型——实现医学影像中脑肿瘤的实例分割。
• 评估模型在三种不同脑肿瘤类型（脑膜瘤、垂体瘤和胶质瘤）上的性能表现。
• 证明使用YOLOv5实现准确且高效脑肿瘤分割在临床诊断中的可行性。

提出的方法

• 该模型采用YOLOv5，一种单阶段目标检测架构，经调整用于MRI扫描中的实例分割。
• 输入数据包括MRI图像及对应的YOLO格式标签文件（图像+文本标注）。
• 网络在包含三种肿瘤类别（脑膜瘤、垂体瘤和胶质瘤）的数据集上进行训练。
• 通过为每个检测到的肿瘤实例分配唯一ID，实现实例分割，即使在同一类别内也能区分。
• 训练在10核GPU上进行，强调运行时效率和模型推理速度。
• 该方法利用YOLOv5的架构实现端到端检测与分割，无需两阶段流水线。

实验结果

研究问题

• RQ1 YOLOv5能否有效适配用于MRI扫描中脑肿瘤的实例分割？
• RQ2 基于YOLOv5的模型在分类和分割三种不同类型的脑肿瘤方面表现如何？
• RQ3 该模型在10核GPU上的推理速度与精度如何，适用于临床部署？
• RQ4 与传统的两阶段分割流水线相比，该模型在效率与性能方面表现如何？
• RQ5 YOLOv5能否在保持实时推理的同时实现具有竞争力的分割精度，适用于医学影像？

主要发现

• YOLOv5模型在三种脑肿瘤类型（脑膜瘤、垂体瘤和胶质瘤）上实现了具有竞争力的分割精度。
• 该模型表现出高效的推理性能，适合在10核GPU上实现实时临床应用。
• 成功实现了实例分割，即使在同一类别内，每个肿瘤实例也分配了唯一ID。
• 该框架减少了对人工分割的依赖，为早期脑肿瘤诊断提供了可扩展的解决方案。
• 结果证实，YOLOv5作为神经科应用中快速且准确的医学图像分割工具具有可行性。