Ahsan Rafia, Shahzadi Iram, Najeeb Faisal, Omer Hammad
Department of Electrical and Computer Engineering, Medical Image Processing Research Group (MIPRG), COMSATS University Islamabad, Islamabad, Pakistan.
OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.
MAGMA. 2025 Feb;38(1):13-22. doi: 10.1007/s10334-024-01203-5. Epub 2024 Sep 4.
Brain tumor detection, classification and segmentation are challenging due to the heterogeneous nature of brain tumors. Different deep learning-based algorithms are available for object detection; however, the performance of detection algorithms on brain tumor data has not been widely explored. Therefore, we aim to compare different object detection algorithms (Faster R-CNN, YOLO & SSD) for brain tumor detection on MRI data. Furthermore, the best-performing detection network is paired with a 2D U-Net for pixel-wise segmentation of abnormal tumor cells.
The proposed model was evaluated on the Brain Tumor Figshare (BTF) dataset, and the best-performing detection network cascaded with 2D U-Net for pixel-wise segmentation of tumors. The best-performing detection network was also fine-tuned on BRATS 2018 data to detect and classify the glioma tumor.
For the detection of three tumor types, YOLOv5 achieved the highest mAP of 89.5% on test data compared to other networks. For segmentation, YOLOv5 combined with 2D U-Net achieved a higher DSC compared to the 2D U-Net alone (DSC: YOLOv5 + 2D U-Net = 88.1%; 2D U-Net = 80.5%). The proposed method was compared with the existing detection and segmentation network i.e. Mask R-CNN and achieved a higher mAP (YOLOv5 + 2D U-Net = 89.5%; Mask R-CNN = 67%) and DSC (YOLOv5 + 2D U-Net = 88.1%; Mask R-CNN = 44.2%).
In this work, we propose a deep-learning-based method for multi-class tumor detection, classification and segmentation that combines YOLOv5 with 2D U-Net. The results show that the proposed method not only detects different types of brain tumors accurately but also delineates the tumor region precisely within the detected bounding box.
由于脑肿瘤的异质性，脑肿瘤的检测、分类和分割具有挑战性。有不同的基于深度学习的算法可用于目标检测；然而，检测算法在脑肿瘤数据上的性能尚未得到广泛探索。因此，我们旨在比较不同的目标检测算法（Faster R-CNN、YOLO和SSD）在MRI数据上进行脑肿瘤检测的效果。此外，性能最佳的检测网络与二维U-Net配对，用于对异常肿瘤细胞进行逐像素分割。
在脑肿瘤图共享（BTF）数据集上评估所提出的模型，性能最佳的检测网络与二维U-Net级联，用于肿瘤的逐像素分割。性能最佳的检测网络也在BRATS 2018数据上进行微调，以检测和分类胶质瘤肿瘤。
对于三种肿瘤类型的检测，与其他网络相比，YOLOv5在测试数据上实现了最高的平均精度均值（mAP），为89.5%。对于分割，与单独的二维U-Net相比，YOLOv5与二维U-Net相结合实现了更高的骰子相似系数（DSC）（DSC：YOLOv5 + 二维U-Net = 88.1%；二维U-Net = 80.5%）。将所提出的方法与现有的检测和分割网络即掩膜区域卷积神经网络（Mask R-CNN）进行比较，实现了更高的mAP（YOLOv5 + 二维U-Net = 89.5%；Mask R-CNN = 67%）和DSC（YOLOv5 + 二维U-Net = 88.1%；Mask R-CNN = 44.2%）。
在这项工作中，我们提出了一种基于深度学习的多类肿瘤检测、分类和分割方法，该方法将YOLOv5与二维U-Net相结合。结果表明，所提出的方法不仅能准确检测不同类型的脑肿瘤，还能在检测到的边界框内精确勾勒出肿瘤区域。
