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A novel automation-assisted cervical cancer reading method based on convolutional neural network

Xiang Yao, Sun Wanxin, Pan Changli, Yan Meng, Yin Zhihua, Liang Yixiong

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 2

611--623

While automation-assisted reading system can improve efficiency, their performance often relies on the success of accurate cell segmentation and hand-craft feature extrac- tion. This paper presents an efficient and totally segmentation-free method for automat-ed cervical cell screening that utilizes modern object detector to directly detect cervical cells or clumps, without the design of specific hand-crafted feature. Specifically, we use the state-of-the-art CNN-based object detection methods, YOLOv3, as our baseline model. In order to improve the classification performance of hard examples which are four highly similar categories, we cascade an additional task-specific classifier. We also investigate the presence of unreliable annotations and coped with them by smoothing the distribu- tion of noisy labels. We comprehensively evaluate our methods on our test set which is consisted of 1014 annotated cervical cell images with size of 4000 3000 and complex cellular situation corresponding to 10 categories. Our model achieves 97.5% sensitivity (Sens) and 67.8% specificity (Spec) on cervical cell image-level screening. Moreover, we obtain a best mean average precision (mAP) of 63.4% on cervical cell-level diagnosis, and improve the average precision (AP) of hard examples which are the most valuable but most difficult to distinguish. Our automation-assisted cervical cell reading system not only achieves cervical cell image-level classification but also provides more detailed location and category reference information of abnormal cells. The results indicate feasible performance of our method, together with the efficiency and robustness, provid- ing a new idea for future development of computer-assisted reading systems in clinical cervical screening.

Estimation of the Fractal Dimension using Tiled Triangular Prism Method for Biological Non-Rectangular Objects

Oszutowska D., Purczyński J.

Przegląd Elektrotechniczny

2012

R. 88, nr 10b

261-263

The aim of the study is application of Triangular Prism Method (TPM) algorithm in computer assisted Papanicolaou smears analysis that is useful in cervical cancer screening. The TPM algorithm allows estimation of the FD (fractal dimension) for optical density of cell nuclei. Selection of the local FD for green color channel gives efficient separation between both cell nuclei classes. Proposed algorithm (Tiled TPM) improves separation by the fractal based estimation using larger area of the cell nuclei.

W artykule zaproponowano nowy algorytm estymacji wymiaru fraktalnego dla obiektów nieregularnych, takich jak jądra komórek bazujący na metodzie pryzm trójkątnych (TPM). Kafelkowy algorytm pryzm trójkątnych pozwala na estymację wymiaru fraktalnego dla większego pola powierzchni obiektu oraz pozwala na estymację lokalnych wymiarów fraktalnych dla małych skal.