Zhou Zichun, Qiu Qingtao, Liu Huiling, Ge Xuanchu, Li Tengxiang, Xing Ligang, Yang Runtao, Yin Yong
School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China.
Department of Radiation Oncology and Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China.
Cancers (Basel). 2023 Sep 6;15(18):4443. doi: 10.3390/cancers15184443.
As a complication of malignant tumors, brain metastasis (BM) seriously threatens patients' survival and quality of life. Accurate detection of BM before determining radiation therapy plans is a paramount task. Due to the small size and heterogeneous number of BMs, their manual diagnosis faces enormous challenges. Thus, MRI-based artificial intelligence-assisted BM diagnosis is significant. Most of the existing deep learning (DL) methods for automatic BM detection try to ensure a good trade-off between precision and recall. However, due to the objective factors of the models, higher recall is often accompanied by higher number of false positive results. In real clinical auxiliary diagnosis, radiation oncologists are required to spend much effort to review these false positive results. In order to reduce false positive results while retaining high accuracy, a modified YOLOv5 algorithm is proposed in this paper. First, in order to focus on the important channels of the feature map, we add a convolutional block attention model to the neck structure. Furthermore, an additional prediction head is introduced for detecting small-size BMs. Finally, to distinguish between cerebral vessels and small-size BMs, a Swin transformer block is embedded into the smallest prediction head. With the introduction of the F2-score index to determine the most appropriate confidence threshold, the proposed method achieves a precision of 0.612 and recall of 0.904. Compared with existing methods, our proposed method shows superior performance with fewer false positive results. It is anticipated that the proposed method could reduce the workload of radiation oncologists in real clinical auxiliary diagnosis.
作为恶性肿瘤的一种并发症，脑转移（BM）严重威胁患者的生存和生活质量。在确定放射治疗方案之前准确检测BM是一项至关重要的任务。由于BM体积小且数量不均一，其人工诊断面临巨大挑战。因此，基于磁共振成像（MRI）的人工智能辅助BM诊断具有重要意义。现有的大多数用于自动检测BM的深度学习（DL）方法都试图在精度和召回率之间取得良好的平衡。然而，由于模型的客观因素，较高的召回率往往伴随着较高数量的假阳性结果。在实际临床辅助诊断中，放射肿瘤学家需要花费大量精力来审查这些假阳性结果。为了在保持高精度的同时减少假阳性结果，本文提出了一种改进的YOLOv5算法。首先，为了关注特征图的重要通道，我们在颈部结构中添加了一个卷积块注意力模型。此外，引入了一个额外的预测头来检测小尺寸的BM。最后，为了区分脑血管和小尺寸的BM，在最小的预测头中嵌入了一个Swin变压器块。通过引入F2分数指标来确定最合适的置信度阈值，所提出的方法实现了0.612的精度和0.904的召回率。与现有方法相比，我们提出的方法在假阳性结果较少的情况下表现出优越的性能。预计所提出的方法可以减少放射肿瘤学家在实际临床辅助诊断中的工作量。
