Embarrassingly Simple Scribble Supervision for 3D Medical Segmentation

arXiv:2403.12834v1 Announce Type: new
Abstract: Traditionally, segmentation algorithms require dense annotations for training, demanding significant annotation efforts, particularly within the 3D medical imaging field. Scribble-supervised learning emerges as a possible solution to this challenge, promising a reduction in annotation efforts when creating large-scale datasets. Recently, a plethora of methods for optimized learning from scribbles have been proposed, but have so far failed to position scribble annotation as a beneficial alternative. We relate this shortcoming to two major issues: 1) the …

abstract algorithms annotation annotations arxiv challenge cs.cv datasets imaging medical medical imaging scale segmentation simple solution supervised learning supervision training type