Previous studies on computer aided detection/diagnosis (CAD) in 4D breast magnetic resonance imaging (MRI) regard lesion detection, segmentation and characterization as separate tasks, and typically require users to manually select 2D MRI slices or regions of interest as the input. In this work, we present a breast MRI CAD system that can handle 4D multimodal breast MRI data, and integrate lesion detection, segmentation and characterization with no user intervention. The proposed CAD system consists of three major stages: region candidate generation, feature extraction and region candidate classification. Breast lesions are firstly extracted as region candidates using the novel 3D multiscale morphological sifting (MMS). The 3D MMS, which uses linear structuring elements to extract lesion-like patterns, can segment lesions from breast images accurately and efficiently. Analytical features are then extracted from all available 4D multimodal breast MRI sequences, including T1-, T2-weighted and DCE sequences, to represent the signal intensity, texture, morphological and enhancement kinetic characteristics of the region candidates. The region candidates are lastly classified as lesion or normal tissue by the random under-sampling boost (RUSboost), and as malignant or benign lesion by the random forest. Evaluated on a breast MRI dataset which contains a total of 117 cases with 95 malignant and 46 benign lesions, the proposed system achieves a true positive rate (TPR) of 0.90 at 3.19 false positives per patient (FPP) for lesion detection and a TPR of 0.91 at a FPP of 2.95 for identifying malignant lesions without any user intervention. The average dice similarity index (DSI) is 0.72 for lesion segmentation. Compared with previously proposed systems evaluated on the same breast MRI dataset, the proposed CAD system achieves a favourable performance in breast lesion detection and characterization.

中文翻译：

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通过乳房 MRI 中的 3D 多尺度形态筛选自动检测、分割和表征

先前关于 4D 乳腺磁共振成像 (MRI) 中计算机辅助检测/诊断 (CAD) 的研究将病变检测、分割和表征作为单独的任务，通常需要用户手动选择 2D MRI 切片或感兴趣区域作为输入。在这项工作中，我们提出了一个乳腺 MRI CAD 系统，该系统可以处理 4D 多模态乳腺 MRI 数据，并在无需用户干预的情况下集成病变检测、分割和表征。所提出的 CAD 系统包括三个主要阶段：区域候选生成、特征提取和区域候选分类。首先使用新颖的 3D 多尺度形态筛选 (MMS) 将乳腺病变提取为候选区域。3D MMS，它使用线性结构元素来提取类似病变的模式，可以准确有效地从乳房图像中分割病变。然后从所有可用的 4D 多模态乳腺 MRI 序列（包括 T1、T2 加权和 DCE 序列）中提取分析特征，以表示候选区域的信号强度、纹理、形态和增强动力学特征。候选区域最后通过随机欠采样增强（RUSboost）分类为病变或正常组织，并通过随机森林分类为恶性或良性病变。在乳腺 MRI 数据集上进行评估，该数据集共包含 117 个病例，其中 95 个恶性病灶和 46 个良性病灶，所提出的系统实现了 0.90 的真阳性率 (TPR)，每个患者的病灶检测假阳性 (FPP) 为 3.19，TPR 为在 FPP 为 2.95 时为 0.91，用于在没有任何用户干预的情况下识别恶性病变。病变分割的平均骰子相似度指数 (DSI) 为 0.72。与先前提出的在相同乳腺 MRI 数据集上评估的系统相比，所提出的 CAD 系统在乳腺病变检测和表征方面取得了良好的性能。