Technol Health Care. 2024;32(6):4965-4982. doi: 10.3233/THC-240158.
More than a million people are affected by brain tumors each year; high-grade gliomas (HGGs) and low-grade gliomas (LGGs) present serious diagnostic and treatment hurdles, resulting in shortened life expectancies. Glioma segmentation is still a significant difficulty in clinical settings, despite improvements in Magnetic Resonance Imaging (MRI) and diagnostic tools. Convolutional neural networks (CNNs) have seen recent advancements that offer promise for increasing segmentation accuracy, addressing the pressing need for improved diagnostic and therapeutic approaches.
The study intended to develop an automated glioma segmentation algorithm using CNN to accurately identify tumor components in MRI images. The goal was to match the accuracy of experienced radiologists with commercial instruments, hence improving diagnostic precision and quantification.
285 MRI scans of high-grade gliomas (HGGs) and low-grade gliomas (LGGs) were analyzed in the study. T1-weighted sequences were utilised for segmentation both pre-and post-contrast agent administration, along with T2-weighted sequences (with and without Fluid Attenuation by Inversion Recovery [FAIRE]). The segmentation performance was assessed with a U-Net network, renowned for its efficacy in medical image segmentation. DICE coefficients were computed for the tumour core with contrast enhancement, the entire tumour, and the tumour nucleus without contrast enhancement.
The U-Net network produced DICE values of 0.7331 for the tumour core with contrast enhancement, 0.8624 for the total tumour, and 0.7267 for the tumour nucleus without contrast enhancement. The results align with previous studies, demonstrating segmentation accuracy on par with professional radiologists and commercially accessible segmentation tools.
The study developed a CNN-based automated segmentation system for gliomas, achieving high accuracy in recognising glioma components in MRI images. The results confirm the ability of CNNs to enhance the accuracy of brain tumour diagnoses, suggesting a promising avenue for future research in medical imaging and diagnostics. This advancement is expected to improve diagnostic processes for clinicians and patients by providing more precise and quantitative results.
每年有超过 100 万人受到脑肿瘤的影响；高级别胶质瘤（HGG）和低级别胶质瘤（LGG）在诊断和治疗方面存在严重的障碍，导致预期寿命缩短。尽管磁共振成像（MRI）和诊断工具有所改进，但胶质瘤分割仍然是临床环境中的一个重大难题。卷积神经网络（CNN）最近取得了进展，有望提高分割准确性，满足提高诊断和治疗方法的迫切需求。
本研究旨在开发一种使用 CNN 的自动胶质瘤分割算法，以准确识别 MRI 图像中的肿瘤成分。目标是使该算法与经验丰富的放射科医生和商业仪器相匹配，从而提高诊断精度和定量分析的准确性。
对 285 例高级别胶质瘤（HGG）和低级别胶质瘤（LGG）的 MRI 扫描进行了分析。分别在增强前和增强后使用 T1 加权序列以及 T2 加权序列（包括不带和带反转恢复液体衰减反转恢复[FAIRE]）进行分割。使用 U-Net 网络评估分割性能，U-Net 网络在医学图像分割方面具有良好的效果。计算肿瘤核心增强、整个肿瘤和无增强肿瘤核心的 DICE 系数。
U-Net 网络对增强肿瘤核心的 DICE 值为 0.7331，对整个肿瘤的 DICE 值为 0.8624，对无增强肿瘤核心的 DICE 值为 0.7267。结果与先前的研究一致，表明该算法的分割准确性与专业放射科医生和商业上可获得的分割工具相当。
本研究开发了一种基于 CNN 的胶质瘤自动分割系统，在 MRI 图像中识别胶质瘤成分方面具有很高的准确性。研究结果证实了 CNN 提高脑肿瘤诊断准确性的能力，为医学成像和诊断领域的未来研究提供了有前途的途径。这一进展有望通过提供更精确和定量的结果，改善临床医生和患者的诊断过程。
