Image compression is a critical tool in decreasing the cost of storage and improving the speed of transmission over the internet. While deep learning applications for natural images widely adopts the usage of lossy compression techniques, it is not widespread for 3D medical images. Using three CT datasets (17 tasks) and one MRI dataset (3 tasks) we demonstrate that lossy compression up to 20 times have no negative impact on segmentation quality with deep neural networks (DNN). In addition, we demonstrate the ability of DNN models trained on compressed data to predict on uncompressed data and vice versa with no quality deterioration.

翻译：图像压缩是降低存储成本、提升网络传输速度的关键技术。尽管深度学习在自然图像处理中广泛采用损失性压缩技术，但该方法在3D医学图像领域尚未普及。通过使用三个CT数据集（17项任务）和一个MRI数据集（3项任务），我们证明在高达20倍的压缩率下，损失性压缩对基于深度神经网络的分割质量不产生负面影响。此外，我们验证了在压缩数据上训练的DNN模型能够无损预测未压缩数据，反之亦然，且不会导致质量下降。