  Modern medical image segmentation methods primarily use discrete
representations in the form of rasterized masks to learn features and generate
predictions. Although effective, this paradigm is spatially inflexible, scales
poorly to higher-resolution images, and lacks direct understanding of object
shapes. To address these limitations, some recent works utilized implicit
neural representations (INRs) to learn continuous representations for
segmentation. However, these methods often directly adopted components designed
for 3D shape reconstruction. More importantly, these formulations were also
constrained to either point-based or global contexts, lacking contextual
understanding or local fine-grained details, respectively--both critical for
accurate segmentation. To remedy this, we propose a novel approach, SwIPE
(Segmentation with Implicit Patch Embeddings), that leverages the advantages of
INRs and predicts shapes at the patch level--rather than at the point level or
image level--to enable both accurate local boundary delineation and global
shape coherence. Extensive evaluations on two tasks (2D polyp segmentation and
3D abdominal organ segmentation) show that SwIPE significantly improves over
recent implicit approaches and outperforms state-of-the-art discrete methods
with over 10x fewer parameters. Our method also demonstrates superior data
efficiency and improved robustness to data shifts across image resolutions and
datasets. Code is available on Github
(https://github.com/charzharr/miccai23-swipe-implicit-segmentation).
