Enhancing Choroidal Nevi Segmentation in Fundus Images Using YOLO

Choroidal nevus often appears as a darkly pigmented, benign ocular lesion, which may progress to malignant forms, such as choroidal melanoma. Prompt and precise diagnosis of choroidal melanoma cannot be overstated as untreated cases can lead to vision loss and even life-threatening metastasis, under-scoring the importance of regular screening of the eye. However, this procedure is performed manually, which can be time-consuming and prone to human errors. Recent advancements in deep learning show potential for detecting eye diseases, including choroidal nevi. However, these models require extensive labelled data, which can be difficult to acquire due to the associated labelling costs. In this paper, we utilize two approaches to tackle these challenges. Firstly, we leverage a pre-trained YOLOv8 segmentation model and train it on both patches and full-size high-resolution colour fundus images. This strategy effectively expands the dataset size and allows the model to focus on the fine details of lesions within individual patches while understanding the general shape of the lesions by analyzing the entire image. Secondly, we use data augmentation to further expand the dataset size and tackle the class imbalance problem. Additionally, through the utilization of post-processing techniques, we enhance the predicted masks by addressing any potential flaws. This approach resulted in a 0.833 Dice Coefficient Score and a 0.714 Intersection Over Union (IOU) in our initial dataset and a Dice score of 0.764 and IOU of 0.618 on our second test set collected from a different site. In both datasets, this approach surpassed the models trained exclusively on full-size images.