Fine-tuned SegFormer for enhanced fetal head segmentation

Abstract

Several challenges in computer vision prompted the research community to propose innovative approaches and unravel new perspectives to optimize deep learning models, thus enhancing the efficiency of vision tasks. Due to its growing applications and promising results in the NLP domain, Transformers models have inspired researchers to adapt this technology to computer vision problems by introducing the Vision Transformers networks. For this purpose, this paper provides a detailed comparative study of the properties of internal representations of Vision Transformers and Convolutional Neural Networks and presents some recent medical hybrid applications. On the other hand, the segmentation task poses various challenges due to the diversity of object position and size. Hence, development of new approaches and techniques is required. In this vein, we adapted the SegFormer model in order to segment the fetal head circumference. Our hybrid model provides a competitive and notable result with a Dice Coefficient of 93.54%, relying only on a small training dataset.