Institute for Intelligent Systems Research and Innovation, Deakin University, Australia.
School of Medicine, Melbourne University, Australia.
Comput Methods Programs Biomed. 2019 Aug;177:17-30. doi: 10.1016/j.cmpb.2019.05.010. Epub 2019 May 15.
Skin melanoma is one of the major health problems in many countries. Dermatologists usually diagnose melanoma by visual inspection of moles. Digital hair removal can provide a non-invasive way to remove hair and hair-like regions as a pre-processing step for skin lesion images. Hair removal has two main steps: hair segmentation and hair gaps inpainting. However, hair segmentation is a challenging task which requires manual tuning of thresholding parameters. Hard-coded threshold leads to over-segmentation (false positives) which in return changes the textural integrity of lesions and or under-segmentation (false negatives) which leaves hair traces and artefacts which affect subsequent diagnosis. Additionally, dermal hair exhibits different characteristics: thin; overlapping; faded; occluded and overlaid on textured lesions.
In this presented paper, we proposed a deep learning approach based on a hybrid network of convolutional and recurrent layers for hair segmentation using weakly labelled data. We utilised the deep encoded features for accurate detection and delineation of hair in skin images. The encoded features are then fed into recurrent neural network layers to encode the spatial dependencies between disjointed patches. Experiments are conducted on a publicly available dataset, called "Towards Melanoma Detection: Challenge". We chose two metrics to evaluate the produced segmentation masks. The first metric is the Jaccard Index which penalises false positives and false negatives. The second metric is the tumour disturb pattern which assesses the overall effect over the lesion texture due to unnecessary inpainting as a result of over segmentation. The qualitative and quantitative evaluations are employed to compare the proposed technique with state-of-the-art methods.
The proposed approach showed superior segmentation accuracy as demonstrated by a Jaccard Index of 77.8% in comparison to a 66.5% reported by the state-of-the-art method. We also achieved tumour disturb pattern as low as 14% compared to 23% for the state-of-the-art method.
The hybrid architecture for segmentation was able to accurately delineate and segment the hair from the background including lesions and the skin using weakly labelled ground truth for training.
皮肤黑色素瘤是许多国家的主要健康问题之一。皮肤科医生通常通过观察痣来诊断黑色素瘤。数字除毛可以提供一种非侵入性的方法来去除毛发和毛发状区域，作为皮肤病变图像的预处理步骤。除毛有两个主要步骤：毛发分割和毛发间隙修复。然而，毛发分割是一项具有挑战性的任务，需要手动调整阈值参数。硬编码的阈值会导致过度分割（假阳性），这反过来又会改变病变的纹理完整性，或者欠分割（假阴性），这会留下毛发痕迹和伪影，影响后续诊断。此外，真皮毛发具有不同的特征：细；重叠；褪色；遮挡和叠加在纹理病变上。
在本文中，我们提出了一种基于卷积和循环层混合网络的深度学习方法，用于使用弱标记数据进行毛发分割。我们利用深度编码特征来准确检测和描绘皮肤图像中的毛发。然后，将编码特征输入到循环神经网络层中，以对不连续补丁之间的空间依赖关系进行编码。实验是在一个名为“迈向黑色素瘤检测：挑战”的公开数据集上进行的。我们选择了两个指标来评估生成的分割掩模。第一个指标是 Jaccard 指数，它惩罚假阳性和假阴性。第二个指标是肿瘤干扰模式，它评估由于过度分割导致的不必要修复对病变纹理的整体影响。采用定性和定量评估方法将所提出的技术与最先进的方法进行比较。
所提出的方法表现出较高的分割准确性，Jaccard 指数为 77.8%，而最先进的方法报告的 Jaccard 指数为 66.5%。我们还实现了低至 14%的肿瘤干扰模式，而最先进的方法为 23%。
用于分割的混合架构能够使用弱标记的真实数据准确地描绘和分割毛发、背景（包括病变和皮肤）。
