Enhancing Few-shot Semantic Segmentation Technique for Medical Image Segmentation

Abstract

When confronted with new, unseen semantic classes, fully-supervised deep learning seg mentation models suffer, as their fine-tuning necessitates enormous quantities of data with annotation. Few-shot semantic segmentation (FSS) offers a solution by enabling the seg mentation of previously unseen semantic classes by using only a few labelled examples and without any reliance on fine-tuning. Existing state-of-the-art FSS approaches primar ily focus on segmenting natural imageset and rely on abundant annotated training data for effective generalization to testing classes that are unseen. Due to the above reasons, this training technique is not practical in clinical imaging situations with scarce annotations. To overcome the difficulties mentioned above, our work proposes enhancement in the exist ing self-supervised FSS framework for medical images called SSL-ALPNet; this framework eliminates the need for annotations which were required in the training phase and leverages superpixel-based pseudo-labels that provide supervision signals and also includes an adap tive local prototype pooling module integrated into the prototypical networks for improvis ing segmentation accuracy. The efficacy ofourmodelisdemonstratedinorgansegmentation using Abdominal-MRI images. Experimental results show that our model, which is an en hancement made to the SSL-ALPNet [1] approach, achieves better segmentation accuracy as compared to other standard Few-Shot-Segmentation methods whose training is based on a manually annotated dataset.