Scribble-Based Interactive Segmentation of Medical Hyperspectral Images

TL;DR

This paper presents a scribble-based interactive segmentation framework for medical hyperspectral images, leveraging deep learning and geodesic distance maps to improve segmentation accuracy amid limited annotated data.

Contribution

It introduces a novel interactive segmentation method combining deep learning features with geodesic distance maps for hyperspectral medical images.

Findings

Geodesic distance maps based on deep learning features outperform other methods.

The proposed framework achieves improved segmentation accuracy.

Deep learning enhances feature extraction for better segmentation results.

Abstract

Hyperspectral imaging (HSI) is an advanced medical imaging modality that captures optical data across a broad spectral range, providing novel insights into the biochemical composition of tissues. HSI may enable precise differentiation between various tissue types and pathologies, making it particularly valuable for tumour detection, tissue classification, and disease diagnosis. Deep learning-based segmentation methods have shown considerable advancements, offering automated and accurate results. However, these methods face challenges with HSI datasets due to limited annotated data and discrepancies from hardware and acquisition techniques~\cite{clancy2020surgical,studier2023heiporspectral}. Variability in clinical protocols also leads to different definitions of structure boundaries. Interactive segmentation methods, utilizing user knowledge and clinical insights, can overcome these issues and achieve precise segmentation results \cite{zhao2013overview}. This work introduces a scribble-based interactive segmentation framework for medical hyperspectral images. The proposed method utilizes deep learning for feature extraction and a geodesic distance map generated from user-provided scribbles to obtain the segmentation results. The experiment results show that utilising the geodesic distance maps based on deep learning-extracted features achieved better segmentation results than geodesic distance maps directly generated from hyperspectral images, reconstructed RGB images, or Euclidean distance maps.