EndoUDA: A modality independent segmentation approach for endoscopy imaging

Gastrointestinal (GI) cancer precursors require frequent monitoring for risk stratification of patients. Automated segmentation methods can help to assess risk areas more accurately, and assist in therapeutic procedures or even removal. In clinical practice, addition to the conventional white-light imaging (WLI), complimentary modalities such as narrow-band imaging (NBI) and fluorescence imaging are used. While, today most segmentation approaches are supervised and only concentrated on a single modality dataset, this work exploits to use a target-independent unsupervised domain adaptation (UDA) technique that is capable to generalize to an unseen target modality. In this context, we propose a novel UDA-based segmentation method that couples the variational autoencoder and U-Net with a common EfficientNet-B4 backbone, and uses a joint loss for latent-space optimization for target samples. We show that our model can generalize to unseen target NBI (target) modality when trained using only WLI (source) modality. Our experiments on both upper and lower GI endoscopy data show the effectiveness of our approach compared to naive supervised approach and state-of-the-art UDA segmentation methods.

Unsupervised Adversarial Domain Adaptation For Barrett's Segmentation

Barrett's oesophagus (BE) is one of the early indicators of esophageal cancer. Patients with BE are monitored and undergo ablation therapies to minimise the risk, thereby making it eminent to identify the BE area precisely. Automated segmentation can help clinical endoscopists to assess and treat BE area more accurately. Endoscopy imaging of BE can include multiple modalities in addition to the conventional white light (WL) modality. Supervised models require large amount of manual annotations incorporating all data variability in the training data. However, it becomes cumbersome, tedious and labour intensive work to generate manual annotations, and additionally modality specific expertise is required. In this work, we aim to alleviate this problem by applying an unsupervised domain adaptation technique (UDA). Here, UDA is trained on white light endoscopy images as source domain and are well-adapted to generalise to produce segmentation on different imaging modalities as target domain, namely narrow band imaging and post acetic-acid WL imaging. Our dataset consists of a total of 871 images consisting of both source and target domains. Our results show that the UDA-based approach outperforms traditional supervised U-Net segmentation by nearly 10% on both Dice similarity coefficient and intersection-over-union.