用深度学习自动识别骨肉瘤CT影像，辅助医生早期诊断。
A Computer-aided Framework for Detecting Osteosarcoma in Computed Tomography Scans
- 构建从预处理到3D可视化全流程的自动化诊断框架
- 在12例患者数据上达AUC 94.8%、特异性94.6%
- 适合医学影像分析与智能辅助诊断方向研究者
骨肉瘤是最常见的原发性骨癌，主要影响青少年和老年人。早期检测对降低骨转移风险至关重要。本文提出一个涵盖预处理、检测、后处理与可视化的机器学习框架，用于通过不同卷积神经网络（CNN）模型分类CT扫描。预处理包括数据增强和病灶区域定位；后处理生成3D骨骼模型并突出显示病变区域。在12名患者的评估中，该框架取得AUC 94.8%和特异性94.6%的性能，验证了其有效性。
原文摘要 · Abstract (English)
Osteosarcoma is the most common primary bone cancer, mainly affecting the youngest and oldest populations. Its detection at early stages is crucial to reduce the probability of developing bone metastasis. In this context, accurate and fast diagnosis is essential to help physicians during the prognosis process. The research goal is to automate the diagnosis of osteosarcoma through a pipeline that includes the preprocessing, detection, postprocessing, and visualization of computed tomography (CT) scans. Thus, this paper presents a machine learning and visualization framework for classifying CT scans using different convolutional neural network (CNN) models. Preprocessing includes data augmentation and identification of the region of interest in scans. Post-processing includes data visualization to render a 3D bone model that highlights the affected area. An evaluation on 12 patients revealed the effectiveness of our framework, obtaining an area under the curve (AUC) of 94.8\% and a specificity of 94.6\%.
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