Automating Vitiligo Skin Lesion Segmentation Using Convolutional Neural Networks

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Automating Vitiligo Skin Lesion Segmentation Using Convolutional Neural Networks


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Automating Vitiligo Skin Lesion Segmentation Using Convolutional Neural Networks

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The measurement of several skin conditions' progression and severity relies on the accurate segmentation (border detection) of lesioned skin images. One such condition is vitiligo. Existing methods for vitiligo image segmentation require manual intervention, which is time-inefficient, labor-intensive, and irreproducible between physicians. We introduce a convolutional neural network (CNN) that quickly and robustly performs such segmentations without manual intervention. We use the U-Net with a modified contracting path to generate an initial segmentation of the lesion. Then, we run the segmentation through the watershed algorithm using high-confidence pixels as "seeds." We train the network on 247 images with a variety of lesion sizes, complexities, and anatomical sites. Our network noticeably outperforms the state-of-the-art U-Net -- scoring a Jaccard Index (JI) of 73.6% (compared to 36.7%). Segmentation occurs in a few seconds, which is a substantial improvement from the previously proposed semi-autonomous watershed approach (2-29 minutes per image).
The measurement of several skin conditions' progression and severity relies on the accurate segmentation (border detection) of lesioned skin images. One such condition is vitiligo. Existing methods for vitiligo image segmentation require manual intervention, which is time-inefficient, labor-intensive, and irreproducible between physicians. We introduce a convolutional neural network (CNN) that quickly and robustly performs such segmentations without manual intervention. We use the U-Net with a modified contracting path to generate an initial segmentation of the lesion. Then, we run the segmentation through the watershed algorithm using high-confidence pixels as "seeds." We train the network on 247 images with a variety of lesion sizes, complexities, and anatomical sites. Our network noticeably outperforms the state-of-the-art U-Net -- scoring a Jaccard Index (JI) of 73.6% (compared to 36.7%). Segmentation occurs in a few seconds, which is a substantial improvement from the previously proposed semi-autonomous watershed approach (2-29 minutes per image).