Pal Anabik, Chaturvedi Akshay, Chandra Aditi, Chatterjee Raghunath, Senapati Swapan, Frangi Alejandro F, Garain Utpal
National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, India.
Comput Biol Med. 2022 Jan;140:105071. doi: 10.1016/j.compbiomed.2021.105071. Epub 2021 Nov 25.
Munro's Microabscess (MM) is the diagnostic hallmark of psoriasis. Neutrophil detection in the Stratum Corneum (SC) of the skin epidermis is an integral part of MM detection in skin biopsy. The microscopic inspection of skin biopsy is a tedious task and staining variations in skin histopathology often hinder human performance to differentiate neutrophils from skin keratinocytes. Motivated from this, we propose a computational framework that can assist human experts and reduce potential errors in diagnosis. The framework first segments the SC layer, and multiple patches are sampled from the segmented regions which are classified to detect neutrophils. Both UNet and CapsNet are used for segmentation and classification. Experiments show that of the two choices, CapsNet, owing to its robustness towards better hierarchical object representation and localisation ability, appears as a better candidate for both segmentation and classification tasks and hence, we termed our framework as MICaps. The training algorithm explores both minimisation of Dice Loss and Focal Loss and makes a comparative study between the two. The proposed framework is validated with our in-house dataset consisting of 290 skin biopsy images. Two different experiments are considered. Under the first protocol, only 3-fold cross-validation is done to directly compare the current results with the state-of-the-art ones. Next, the performance of the system on a held-out data set is reported. The experimental results show that MICaps improves the state-of-the-art diagnosis performance by 3.27% (maximum) and reduces the number of model parameters by 50%.
蒙罗微脓肿（MM）是银屑病的诊断标志。在皮肤活检中检测MM时，皮肤表皮角质层（SC）中的中性粒细胞检测是不可或缺的一部分。皮肤活检的显微镜检查是一项繁琐的任务，皮肤组织病理学中的染色差异常常阻碍人们区分中性粒细胞和皮肤角质形成细胞。受此启发，我们提出了一个计算框架，该框架可以辅助人类专家并减少诊断中的潜在错误。该框架首先分割SC层，然后从分割区域中采样多个小块进行分类以检测中性粒细胞。UNet和CapsNet都用于分割和分类。实验表明，在这两种选择中，CapsNet由于其对更好的分层对象表示的鲁棒性和定位能力，在分割和分类任务中似乎是更好的选择，因此，我们将我们的框架称为MICaps。训练算法探索了Dice损失和焦点损失的最小化，并对两者进行了比较研究。所提出的框架使用我们包含290张皮肤活检图像的内部数据集进行了验证。考虑了两个不同的实验。在第一个协议下，仅进行3折交叉验证以直接将当前结果与最先进的结果进行比较。接下来，报告系统在一个留出的数据集上的性能。实验结果表明，MICaps将最先进的诊断性能提高了3.27%（最大值），并将模型参数数量减少了50%。
