Implicit Neural Representations for Medical Imaging Segmentation

Abstract

3D signals in medical imaging, such as CT scans, are usually parameterized as a discrete grid of voxels. For instance, existing state-of-the-art organ segmentation methods learn discrete segmentation maps. Unfortunately, the memory requirements of such methods grow cubically with increasing spatial resolution, which makes them unsuitable for processing high resolution scans. To overcome this, we design an Implicit Organ Segmentation Network (IOSNet) that utilizes continuous Implicit Neural Representations and has several useful properties. Firstly, the IOSNet decoder memory is roughly constant and independent of the spatial resolution since it parameterizes the segmentation map as a continuous function. Secondly, IOSNet converges much faster than discrete voxel based methods due to its ability to accurately segment organs irrespective of organ sizes, thereby alleviating size imbalance issues without requiring any auxiliary tricks. Thirdly, IOSNet naturally supports super-resolution (i.e. sampling at arbitrary resolutions during inference) due to its continuous learnt representations. Moreover, despite using a simple lightweight decoder, IOSNet consistently outperforms the discrete specialized segmentation architecture UNet. Hence, our approach demonstrates that Implicit Neural Representations are well-suited for medical imaging applications, especially for processing high-resolution 3D medical scans.