LS-Net: lightweight segmentation network for dermatological epidermal segmentation in optical coherence tomography imaging

Optical coherence tomography (OCT) can be an important tool for non-invasivedermatological evaluation, providing useful data on epidermal integrity for diagnosing skindiseases. Despite its benefits, OCT’s utility is limited by the challenges of accurate, fastepidermal segmentation due to the skin morphological diversity. To address this, we introducea lightweight segmentation network (LS-Net), a novel deep learning model that combines therobust local feature extraction abilities of Convolution Neural Network and the long-terminformation processing capabilities of Vision Transformer. LS-Net has a depth-wiseconvolutional transformer for enhanced spatial contextualization and a squeeze-and-excitationblock for feature recalibration, ensuring precise segmentation while maintaining computationalefficiency. Our network outperforms existing methods, demonstrating high segmentationaccuracy (mean Dice: 0.9624 and mean IoU: 0.9468) with significantly reduced computationaldemands (floating point operations: 1.131 G). We further validate LS-Net on our acquireddataset, showing its effectiveness in various skin sites (e.g., face, palm) under realistic clinicalconditions. This model promises to enhance the diagnostic capabilities of OCT, making it avaluable tool for dermatological practice.