  Volumetric medical segmentation is a critical component of 3D medical image
analysis that delineates different semantic regions. Deep neural networks have
significantly improved volumetric medical segmentation, but they generally
require large-scale annotated data to achieve better performance, which can be
expensive and prohibitive to obtain. To address this limitation, existing works
typically perform transfer learning or design dedicated pretraining-finetuning
stages to learn representative features. However, the mismatch between the
source and target domain can make it challenging to learn optimal
representation for volumetric data, while the multi-stage training demands
higher compute as well as careful selection of stage-specific design choices.
In contrast, we propose a universal training framework called MedContext that
is architecture-agnostic and can be incorporated into any existing training
framework for 3D medical segmentation. Our approach effectively learns self
supervised contextual cues jointly with the supervised voxel segmentation task
without requiring large-scale annotated volumetric medical data or dedicated
pretraining-finetuning stages. The proposed approach induces contextual
knowledge in the network by learning to reconstruct the missing organ or parts
of an organ in the output segmentation space. The effectiveness of MedContext
is validated across multiple 3D medical datasets and four state-of-the-art
model architectures. Our approach demonstrates consistent gains in segmentation
performance across datasets and different architectures even in few-shot data
scenarios. Our code and pretrained models are available at
https://github.com/hananshafi/MedContext
