Department of CSE, BUET, ECE Building, West Palashi, Dhaka, Bangladesh.
Department of Pathology, Bangabandhu Sheikh Mujib Medical University, Shahabag, Dhaka, Bangladesh.
BMC Med Imaging. 2021 Jan 28;21(1):15. doi: 10.1186/s12880-020-00533-9.
Segmentation of nuclei in cervical cytology pap smear images is a crucial stage in automated cervical cancer screening. The task itself is challenging due to the presence of cervical cells with spurious edges, overlapping cells, neutrophils, and artifacts.
After the initial preprocessing steps of adaptive thresholding, in our approach, the image passes through a convolution filter to filter out some noise. Then, contours from the resultant image are filtered by their distinctive contour properties followed by a nucleus size recovery procedure based on contour average intensity value.
We evaluate our method on a public (benchmark) dataset collected from ISBI and also a private real dataset. The results show that our algorithm outperforms other state-of-the-art methods in nucleus segmentation on the ISBI dataset with a precision of 0.978 and recall of 0.933. A promising precision of 0.770 and a formidable recall of 0.886 on the private real dataset indicate that our algorithm can effectively detect and segment nuclei on real cervical cytology images. Tuning various parameters, the precision could be increased to as high as 0.949 with an acceptable decrease of recall to 0.759. Our method also managed an Aggregated Jaccard Index of 0.681 outperforming other state-of-the-art methods on the real dataset.
We have proposed a contour property-based approach for segmentation of nuclei. Our algorithm has several tunable parameters and is flexible enough to adapt to real practical scenarios and requirements.
在自动化宫颈癌筛查中，对宫颈细胞学巴氏涂片图像中的细胞核进行分割是至关重要的阶段。由于存在具有虚假边缘、重叠细胞、中性粒细胞和伪影的宫颈细胞，因此该任务本身具有挑战性。
在我们的方法中，经过自适应阈值处理的初始预处理步骤后，图像会通过卷积滤波器进行过滤以消除一些噪声。然后，根据轮廓的独特轮廓属性过滤来自结果图像的轮廓，然后根据轮廓平均强度值进行核大小恢复过程。
我们在从 ISBI 收集的公共（基准）数据集和私人真实数据集上评估了我们的方法。结果表明，我们的算法在 ISBI 数据集上的细胞核分割方面优于其他最先进的方法，精度为 0.978，召回率为 0.933。在私人真实数据集上，我们的算法具有有希望的 0.770 精度和强大的 0.886 召回率，表明我们的算法可以有效地检测和分割真实宫颈细胞学图像中的细胞核。调整各种参数后，精度可以提高到 0.949，而召回率可以降低到 0.759，这是可以接受的。我们的方法在真实数据集上的综合 Jaccard 指数也达到了 0.681，优于其他最先进的方法。
我们提出了一种基于轮廓属性的细胞核分割方法。我们的算法具有几个可调参数，足够灵活，可以适应实际的实际情况和要求。
