Personalized 3D vascular models can aid in a range of diagnostic, prognostic,
and treatment-planning tasks relevant to cardiovascular disease management.
Deep learning provides a means to automatically obtain such models. Ideally, a
user should have control over the exact region of interest (ROI) to be included
in a vascular model, and the model should be watertight and highly accurate. To
this end, we propose a combination of a global controller leveraging voxel mask
segmentations to provide boundary conditions for vessels of interest to a
local, iterative vessel segmentation model. We introduce the preservation of
scale- and rotational symmetries in the local segmentation model, leading to
generalisation to vessels of unseen sizes and orientations. Combined with the
global controller, this enables flexible 3D vascular model building, without
additional retraining. We demonstrate the potential of our method on a dataset
containing abdominal aortic aneurysms (AAAs). Our method performs on par with a
state-of-the-art segmentation model in the segmentation of AAAs, iliac arteries
and renal arteries, while providing a watertight, smooth surface segmentation.
Moreover, we demonstrate that by adapting the global controller, we can easily
extend vessel sections in the 3D model.

个性化 3D 血管模型的深度学习自动化方法通过组合全局控制器和局部分割模型，实现了水密且高精度的血管分割，能够灵活构建血管模型并扩展血管部分。在腹主动脉瘤、髂动脉和肾动脉的分割中与最先进的模型相媲美。

全局控制的局部 SO (3)- 等变尺度不变血管分割

Global Control for Local SO(3)-Equivariant Scale-Invariant Vessel  Segmentation

We propose a deep learning-based technique for detection and quantification
of abdominal aortic aneurysms (AAAs). The condition, which leads to more than
10,000 deaths per year in the United States, is asymptomatic, often detected
incidentally, and often missed by radiologists. Our model architecture is a
modified 3D U-Net combined with ellipse fitting that performs aorta
segmentation and AAA detection. The study uses 321 abdominal-pelvic CT
examinations performed by Massachusetts General Hospital Department of
Radiology for training and validation. The model is then further tested for
generalizability on a separate set of 57 examinations with differing patient
demographics and acquisition characteristics than the original dataset. DeepAAA
achieves high performance on both sets of data (sensitivity/specificity
0.91/0.95 and 0.85 / 1.0 respectively), on contrast and non-contrast CT scans
and works with image volumes with varying numbers of images. We find that
DeepAAA exceeds literature-reported performance of radiologists on incidental
AAA detection. It is expected that the model can serve as an effective
background detector in routine CT examinations to prevent incidental AAAs from
being missed.

本研究提出了一种基于深度学习的技术，用于检测和量化腹主动脉瘤，该模型架构是一个改进的 3D U-Net 算法与椭圆拟合相结合，可用于主动脉分割和腹主动脉瘤检测，模型经过 321 个腹部 - 盆腔 CT 检查的训练和验证，在 57 个新的检查数据上也具备很高的性能。