SRM Superpixel Merging Framework for Precise Segmentation of Cervical Nucleus

Saha, Ratna; Bajger, Mariusz

doi:10.1109/dicta47822.2019.8945887

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…Later on, Saha et al [17] also proposed a method of segmenting cervical nuclei by merging the oversegmented SLIC superpixel regions based on pairwise regional contrast and image gradient contour evaluations. Their more recent work [18] proposes to segment nuclei by merging superpixels generated by the statistical region merging (SRM) algorithm using pairwise regional contrasts and gradient boundaries. Braz and Lotufo [19] used a deep learning convolutional network to detect and segment nuclei from pap smear images.…”

Section: Introductionmentioning

confidence: 99%

A contour property based approach to segment nuclei in cervical cytology images

et al. 2021

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Background 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. Methods 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. Results 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. Conclusion 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.

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Section: Introductionmentioning

confidence: 99%

A contour property based approach to segment nuclei in cervical cytology images

et al. 2021

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“…Traditional methods for cytological nuclei segmentation are based on edge enhancement [22], thresholding [17,[23][24][25], clustering [8,9,26,27], morphological features and marker controlled watershed [28,29]. [22] provides a cervical nucleus and cytoplasm detector based on edge enhancement.…”

Section: Handcrafted Methodsmentioning

confidence: 99%

“…A gap search method was applied to minimize time complexity. Sahe et al [ 9 ] utilized a superpixel merging approach for accurate segmentation of crowded cervical nuclei. The superpixel is obtained through the statistical region merging (SRM) technique through the pair-wise regional control threshold of SLIC (Simple Linear Iterative Clustering) superpixel.…”

Section: Related Workmentioning

confidence: 99%

“…Recent studies [ 9 ] have shown some improvements in the isolation of clumping nuclei and cytoplasm from cervical cells. The datasets used in most of these studies for the delineation of nuclei include the liquid Based Cytology (LBC) Pap smear dataset and Herlev dataset for single-cell delineation and Shenzhen University (SZU), ISBI2014, and ISBI2015 datasets for multi-cell examination.…”

Section: Introductionmentioning

confidence: 99%

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Cervical cell’s nucleus segmentation through an improved UNet architecture

Rasheed,

Shirazi,

Umar

et al. 2023

PLoS ONE

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Precise segmentation of the nucleus is vital for computer-aided diagnosis (CAD) in cervical cytology. Automated delineation of the cervical nucleus has notorious challenges due to clumped cells, color variation, noise, and fuzzy boundaries. Due to its standout performance in medical image analysis, deep learning has gained attention from other techniques. We have proposed a deep learning model, namely C-UNet (Cervical-UNet), to segment cervical nuclei from overlapped, fuzzy, and blurred cervical cell smear images. Cross-scale features integration based on a bi-directional feature pyramid network (BiFPN) and wide context unit are used in the encoder of classic UNet architecture to learn spatial and local features. The decoder of the improved network has two inter-connected decoders that mutually optimize and integrate these features to produce segmentation masks. Each component of the proposed C-UNet is extensively evaluated to judge its effectiveness on a complex cervical cell dataset. Different data augmentation techniques were employed to enhance the proposed model’s training. Experimental results have shown that the proposed model outperformed extant models, i.e., CGAN (Conditional Generative Adversarial Network), DeepLabv3, Mask-RCNN (Region-Based Convolutional Neural Network), and FCN (Fully Connected Network), on the employed dataset used in this study and ISBI-2014 (International Symposium on Biomedical Imaging 2014), ISBI-2015 datasets. The C-UNet achieved an object-level accuracy of 93%, pixel-level accuracy of 92.56%, object-level recall of 95.32%, pixel-level recall of 92.27%, Dice coefficient of 93.12%, and F1-score of 94.96% on complex cervical images dataset.

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“…These wrong labels seriously affect the accuracy of model evaluation and destabilize benchmarks, which will ultimately spill over model selection and deployment. For example, the deployed model in learning-based computer-aided diagnosis (Saha et al, 2019 ; Song et al, 2019 , 2020 ; Wan et al, 2019 ; Zhang et al, 2020 ) is selected from many candidate models based on evaluation accuracy, which means that inaccurate annotations may ultimately affect accurate diagnosis. To mitigate labeling errors, an image is often annotated by multiple annotators (Arbelaez et al, 2010 ; Almazroa et al, 2017 ; Zhang et al, 2019 ), which generates multiple labels for one image.…”

Section: Introductionmentioning

confidence: 99%

Local Label Point Correction for Edge Detection of Overlapping Cervical Cells

Liu

Fan

Wang

et al. 2022

Front. Neuroinform.

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Accurate labeling is essential for supervised deep learning methods. However, it is almost impossible to accurately and manually annotate thousands of images, which results in many labeling errors for most datasets. We proposes a local label point correction (LLPC) method to improve annotation quality for edge detection and image segmentation tasks. Our algorithm contains three steps: gradient-guided point correction, point interpolation, and local point smoothing. We correct the labels of object contours by moving the annotated points to the pixel gradient peaks. This can improve the edge localization accuracy, but it also causes unsmooth contours due to the interference of image noise. Therefore, we design a point smoothing method based on local linear fitting to smooth the corrected edge. To verify the effectiveness of our LLPC, we construct a largest overlapping cervical cell edge detection dataset (CCEDD) with higher precision label corrected by our label correction method. Our LLPC only needs to set three parameters, but yields 30–40% average precision improvement on multiple networks. The qualitative and quantitative experimental results show that our LLPC can improve the quality of manual labels and the accuracy of overlapping cell edge detection. We hope that our study will give a strong boost to the development of the label correction for edge detection and image segmentation. We will release the dataset and code at: https://github.com/nachifur/LLPC.

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SRM Superpixel Merging Framework for Precise Segmentation of Cervical Nucleus

Cited by 8 publications

References 31 publications

A contour property based approach to segment nuclei in cervical cytology images

A contour property based approach to segment nuclei in cervical cytology images

Cervical cell’s nucleus segmentation through an improved UNet architecture

Local Label Point Correction for Edge Detection of Overlapping Cervical Cells

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