LAMA: Lesion-Aware Mixup Augmentation for Skin Lesion Segmentation

• Published in: Journal of digital imaging 2024-08, Vol.37 (4), p.1812-1823
• Main Authors: Lama, Norsang, Stanley, Ronald Joe, Lama, Binita, Maurya, Akanksha, Nambisan, Anand, Hagerty, Jason, Phan, Thanh, Van Stoecker, William
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial neural networks; Data augmentation; Databases, Factual; Datasets; Deep Learning; Dermatology; Dermoscopy - methods; Effectiveness; Humans; Image Interpretation, Computer-Assisted - methods; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Informatics; Information retrieval; Lesions; Machine learning; Medical diagnosis; Melanoma; Methods; Neural networks; Neural Networks, Computer; Performance evaluation; Skin - diagnostic imaging; Skin - pathology; Skin cancer; Skin diseases; Skin Diseases - diagnostic imaging; Skin Diseases - pathology; Skin lesions; Skin Neoplasms - diagnostic imaging; Skin Neoplasms - pathology; Skin tests
• ISSN: 2948-2933; 0897-1889; 2948-2925; 2948-2933; 1618-727X
• DOI: 10.1007/s10278-024-01000-5

Deep learning can exceed dermatologists' diagnostic accuracy in experimental image environments. However, inaccurate segmentation of images with multiple skin lesions can be seen with current methods. Thus, information present in multiple-lesion images, available to specialists, is not retrievable by machine learning. While skin lesion images generally capture a single lesion, there may be cases in which a patient's skin variation may be identified as skin lesions, leading to multiple false positive segmentations in a single image. Conversely, image segmentation methods may find only one region and may not capture multiple lesions in an image. To remedy these problems, we propose a novel and effective data augmentation technique for skin lesion segmentation in dermoscopic images with multiple lesions. The lesion-aware mixup augmentation (LAMA) method generates a synthetic multi-lesion image by mixing two or more lesion images from the training set. We used the publicly available International Skin Imaging Collaboration (ISIC) 2017 Challenge skin lesion segmentation dataset to train the deep neural network with the proposed LAMA method. As none of the previous skin lesion datasets (including ISIC 2017) has considered multiple lesions per image, we created a new multi-lesion (MuLe) segmentation dataset utilizing publicly available ISIC 2020 skin lesion images with multiple lesions per image. MuLe was used as a test set to evaluate the effectiveness of the proposed method. Our test results show that the proposed method improved the Jaccard score 8.3% from 0.687 to 0.744 and the Dice score 5% from 0.7923 to 0.8321 over a baseline model on MuLe test images. On the single-lesion ISIC 2017 test images, LAMA improved the baseline model's segmentation performance by 0.08%, raising the Jaccard score from 0.7947 to 0.8013 and the Dice score 0.6% from 0.8714 to 0.8766. The experimental results showed that LAMA improved the segmentation accuracy on both single-lesion and multi-lesion dermoscopic images. The proposed LAMA technique warrants further study.