Magnetic Resonance Imaging MRI is a relevant tool for breast cancer screening. Moreover, an accurate 3D segmentation of breast tumors from MRI scans plays a key role in the analysis of the disease. In this manuscript, we propose a novel 3D automatic method for segmenting MRI breast tumors, called 3D Automatic Levels Propagation Approach (3D-ALPA). The proposed method performs the segmentation automatically in two steps: in the first step, the entire MRI volume to process is segmented slice by slice. Specifically, using a new automatic approach called 2D Automatic Levels Propagation Approach (2D-ALPA) which is an improved version of a previous semi-automatic approach, named 2D Levels Propagation Approach (2D-LPA). In the second step, the partial segmentations obtained after the application of 2D-ALPA are recombined to rebuild the complete volume(s) of tumor(s). 3D-ALPA has many characteristics, mainly: it is an automatic method which can take into consideration multi-tumor segmentation, and it has the property to be easily applicable according to the Axial, Coronal, as well as Sagittal planes. Therefore, it offers a multi-view representation of the segmented tumor(s). To validate the new 3D-ALPA method, we have firstly performed tests on a 2D private dataset composed of eighteen patients to estimate the accuracy of the new 2D-ALPA in comparison to the previous 2D-LPA. The obtained results have been in favor of the proposed 2D-ALPA, showing hence an improvement in accuracy after integrating the automatization in the 2D-ALPA approach. Then, we have evaluated the complete 3D-ALPA method on a 3D private dataset constituted of MRI exams of twenty-two patients having real breast tumors of different types, and on the public RIDER dataset. Essentially, 3D-ALPA has been evaluated regarding two main features: segmentation accuracy and running time, by considering two kinds of breast tumors: non-enhanced and enhanced tumors. The experimental studies have shown that 3D-ALPA has produced better results for the both kinds of tumors than a recent and concurrent method in the literature that addresses the same problematic.

磁共振成像MRI是乳腺癌筛查的相关工具。此外，通过MRI扫描对乳腺肿瘤进行精确的3D分割在疾病分析中起着关键作用。在此手稿中，我们提出了一种用于分割MRI乳腺肿瘤的新颖3D自动方法，称为3D自动水平传播方法（3D-ALPA）。所提出的方法分两步自动执行分割：第一步，将要处理的整个MRI体积逐片分割。具体来说，使用称为2D自动水平传播方法（2D-ALPA）的新自动方法，该方法是先前的半自动方法的改进版本，称为2D水平传播方法（2D-LPA）。在第二步中，将应用2D-ALPA之后获得的部分分割重组以重建完整的肿瘤体积。3D-ALPA具有许多特点，主要是：它是一种可以考虑多肿瘤分割的自动方法，并且具有易于根据轴向，冠状面以及矢状面应用的特性。因此，它提供了分割肿瘤的多视图表示。为了验证新的3D-ALPA方法，我们首先对由18位患者组成的2D私人数据集进行了测试，以评估新2D-ALPA与以前的2D-LPA的准确性。获得的结果有利于提出的2D-ALPA，因此表明在将自动化方法集成到2D-ALPA方法中后，准确性有所提高。然后，我们已经对由22位患有不同类型的真实乳腺肿瘤的MRI检查组成的3D私人数据集和公共RIDER数据集评估了完整的3D-ALPA方法。本质上，通过考虑两种乳腺肿瘤：未增强和增强的肿瘤，对3D-ALPA的两个主要特征进行了评估：分割的准确性和运行时间。实验研究表明3D-ALPA对这两种肿瘤都产生了比文献中针对相同问题的最新并发方法更好的结果。通过考虑两种乳腺肿瘤：非增强型和增强型肿瘤，确定分割的准确性和运行时间。实验研究表明3D-ALPA对这两种肿瘤都产生了比文献中针对同一问题的最新且并行的方法更好的结果。通过考虑两种乳腺肿瘤：非增强型和增强型肿瘤，确定分割的准确性和运行时间。实验研究表明3D-ALPA对这两种肿瘤都产生了比文献中针对相同问题的最新并发方法更好的结果。