Akbarpour Mohammadesmaeil, Fazlollahiaghamalek Hamed, Barati Mahdi, Hashemi Kamangar Mehrdad, Mandal Mrinal
Electrical and Computer Engineering, University of Alberta, Edmonton AB T6G 2R3, Canada.
Faculty of Engineering and Technology, Shomal University, Amol 4616184596, Iran.
J Imaging. 2025 Aug 15;11(8):274. doi: 10.3390/jimaging11080274.
Histopathological images play a crucial role in diagnosing skin cancer. However, due to the very large size of digital histopathological images (typically in the order of billion pixels), manual image analysis is tedious and time-consuming. Therefore, there has been significant interest in developing Artificial Intelligence (AI)-enabled computer-aided diagnosis (CAD) techniques for skin cancer detection. Due to the diversity of uncertain cell boundaries, automated nuclei segmentation of histopathological images remains challenging. Automating the identification of abnormal cell nuclei and analyzing their distribution across multiple tissue sections can significantly expedite comprehensive diagnostic assessments. In this paper, a deep neural network (DNN)-based technique is proposed to segment nuclei and detect melanoma in histopathological images. To achieve a robust performance, a test image is first augmented by various geometric operations. The augmented images are then passed through the DNN and the individual outputs are combined to obtain the final nuclei-segmented image. A morphological technique is then applied on the nuclei-segmented image to detect the melanoma region in the image. Experimental results show that the proposed technique can achieve a Dice score of 91.61% and 87.9% for nuclei segmentation and melanoma detection, respectively.
组织病理学图像在皮肤癌诊断中起着至关重要的作用。然而，由于数字组织病理学图像尺寸非常大（通常达到数十亿像素量级），手动图像分析既繁琐又耗时。因此，开发用于皮肤癌检测的人工智能（AI）支持的计算机辅助诊断（CAD）技术引起了广泛关注。由于不确定的细胞边界具有多样性，组织病理学图像的细胞核自动分割仍然具有挑战性。自动识别异常细胞核并分析它们在多个组织切片中的分布可以显著加快全面诊断评估。本文提出了一种基于深度神经网络（DNN）的技术，用于在组织病理学图像中分割细胞核并检测黑色素瘤。为了实现稳健的性能，首先通过各种几何操作对测试图像进行增强。然后将增强后的图像输入DNN，将各个输出进行组合以获得最终的细胞核分割图像。接着对细胞核分割图像应用形态学技术来检测图像中的黑色素瘤区域。实验结果表明，所提出的技术在细胞核分割和黑色素瘤检测方面分别可以达到91.61%和87.9%的Dice分数。
