Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:3911-3914. doi: 10.1109/EMBC48229.2022.9871748.
Ultrasound (US) is a medical imaging modality widely used for diagnosis, monitoring, and guidance of surgical procedures. However, the accurate interpretation of US images is a challenging task. Recently, portable 2D US devices enhanced with Artificial intelligence (AI) methods to identify, in real-time, specific organs are widely spreading worldwide. Nevertheless, the number of available methods that effectively work in such devices is still limited. In this work, we evaluate the performance of the U-NET architecture to segment the kidney in 2D US images. To accomplish this task, we studied the possibility of using multiple sliced images extracted from 3D US volumes to achieve a large, variable, and multi-view dataset of 2D images. The proposed methodology was tested with a dataset of 66 3D US volumes, divided in 51 for training, 5 for validation, and 10 for testing. From the volumes, 3792 2D sliced images were extracted. Two experiments were conducted, namely: (i) using the entire database (WWKD); and (ii) using images where the kidney area is > 500 mm2 (500KD). As a proof-of-concept, the potential of our strategy was tested in real 2D images (acquired with 2D probes). An average error of 2.88 ± 2.63 mm in the testing dataset was registered. Moreover, satisfactory results were obtained in our initial proof-of-concept using pure 2D images. In short, the proposed method proved, in this preliminary study, its potential interest for clinical practice. Further studies are required to evaluate the real performance of the proposed methodology. Clinical Relevance- In this work a deep learning methodology to segment the kidney in 2D US images is presented. It may be a relevant feature to be included in the recent portable US ecosystems easing the interpretation of image and consequently the clinical analysis.
超声（US）是一种广泛用于诊断、监测和指导手术过程的医学成像方式。然而，准确解释 US 图像是一项具有挑战性的任务。最近，带有人工智能（AI）方法的便携式 2D US 设备可实时识别特定器官，在全球范围内广泛传播。然而，能够在这种设备中有效工作的方法数量仍然有限。在这项工作中，我们评估了 U-NET 架构在 2D US 图像中分割肾脏的性能。为了完成这项任务，我们研究了使用从 3D US 体数据集中提取的多个切片图像来实现 2D 图像的大型、可变和多视图数据集的可能性。所提出的方法学使用了 66 个 3D US 体数据集进行了测试，分为 51 个用于训练，5 个用于验证，10 个用于测试。从这些体数据集中提取了 3792 张 2D 切片图像。进行了两项实验，即：（i）使用整个数据库（WWKD）；和（ii）使用肾脏区域>500mm2 的图像（500KD）。作为概念验证，我们的策略潜力在实际的 2D 图像（使用 2D 探头获取）中进行了测试。在测试数据集上记录的平均误差为 2.88±2.63mm。此外，在我们使用纯 2D 图像的初步概念验证中，获得了令人满意的结果。总之，在这项初步研究中，所提出的方法证明了其在临床实践中的潜在兴趣。需要进一步的研究来评估所提出的方法的实际性能。临床意义- 在这项工作中，提出了一种用于分割 2D US 图像中肾脏的深度学习方法。它可能是一个相关的特征，可被包含在最近的便携式 US 生态系统中，以简化图像的解释，从而简化临床分析。
