Over the last decade, convolutional neural networks have emerged and advanced
the state-of-the-art in various image analysis and computer vision
applications. The performance of 2D image classification networks is constantly
improving and being trained on databases made of millions of natural images.
However, progress in medical image analysis has been hindered by limited
annotated data and acquisition constraints. These limitations are even more
pronounced given the volumetry of medical imaging data. In this paper, we
introduce an efficient way to transfer the efficiency of a 2D classification
network trained on natural images to 2D, 3D uni- and multi-modal medical image
segmentation applications. In this direction, we designed novel architectures
based on two key principles: weight transfer by embedding a 2D pre-trained
encoder into a higher dimensional U-Net, and dimensional transfer by expanding
a 2D segmentation network into a higher dimension one. The proposed networks
were tested on benchmarks comprising different modalities: MR, CT, and
ultrasound images. Our 2D network ranked first on the CAMUS challenge dedicated
to echo-cardiographic data segmentation and surpassed the state-of-the-art.
Regarding 2D/3D MR and CT abdominal images from the CHAOS challenge, our
approach largely outperformed the other 2D-based methods described in the
challenge paper on Dice, RAVD, ASSD, and MSSD scores and ranked third on the
online evaluation platform. Our 3D network applied to the BraTS 2022
competition also achieved promising results, reaching an average Dice score of
91.69% (91.22%) for the whole tumor, 83.23% (84.77%) for the tumor core, and
81.75% (83.88%) for enhanced tumor using the approach based on weight
(dimensional) transfer. Experimental and qualitative results illustrate the
effectiveness of our methods for multi-dimensional medical image segmentation.

本论文介绍了一种将自然图像上训练的 2D 分类网络的效率转移到 2D、3D 单模态和多模态医学图像分割应用中的高效方法，该方法基于权重转移和维度转移的两个关键原则，实验证明其在多维医学图像分割方面表现出色。