Nucleus segmentation and classification using residual SE-UNet and feature concatenation approach incervical cytopathology cell images

Abstract

Introduction: Pap smear is considered to be the primary examination for the diagnosis of cervical cancer. But the analysis of pap smear slides is a time-consuming task and tedious as it requires manual intervention. The diagnostic efficiency depends on the medical expertise of the pathologist, and human error often hinders the diagnosis. Automated segmentation and classification of cervical nuclei will help diagnose cervical cancer in earlier stages. Materials and Methods: The proposed methodology includes three models: a Residual-Squeeze-and-Excitation-module based segmentation model, a fusion-based feature extraction model, and a Multi-layer Perceptron classification model. In the fusion-based feature extraction model, three sets of deep features are extracted from these segmented nuclei using the pre-trained and fine-tuned VGG19, VGG-F, and CaffeNet models, and two hand-crafted descriptors, Bag-of-Features and Linear-Binary-Patterns, are extracted for each image. For this work, Herlev, SIPaKMeD, and ISBI2014 datasets are used for evaluation. The Herlev datasetis used for evaluating both segmentation and classification models. Whereas the SIPaKMeD and ISBI2014 are used for evaluating the classification model, and the segmentation model respectively. Results: The segmentation network enhanced the precision and ZSI by 2.04%, and 2.00% on the Herlev dataset, and the precision and recall by 0.68%, and 2.59% on the ISBI2014 dataset. The classification approach enhanced the accuracy, recall, and specificity by 0.59%, 0.47%, and 1.15% on the Herlev dataset, and by 0.02%, 0.15%, and 0.22% on the SIPaKMed dataset. Conclusion: The experiments demonstrate that the proposed work achieves promising performance on segmentation and classification in cervical cytopathology cell images..

A nomogram prediction model of pseudomyxoma peritonei established based on new prognostic factors of HE stained pathological images analysis

“…Papanicolaou's smear test is used to diagnose uterine and cervical cancer by detecting nuclear chromatin staining, size, and shape changes. 38 Whitney et al. 39 found that nuclear shape, texture and structure could independently predict the risk of recurrence in ER+ breast cancer patients.…”

“…Papanicolaou's smear test is used to diagnose uterine and cervical cancer by detecting nuclear chromatin staining, size, and shape changes. 38 Whitney et al 39 found that nuclear shape, texture and structure could independently predict the risk of recurrence in ER+ breast cancer patients. This study showed that TCs nuclei circularity variance, which mainly evaluated the uniformity of nuclear roundness, was an independent prognosticator, and the greater the difference in nuclear roundness, the better the prognosis of PMP patients.…”

“…Nuclear characteristics in PIs are basis for benign or malignant detection and grading of many solid tumors. Papanicolaou's smear test is used to diagnose uterine and cervical cancer by detecting nuclear chromatin staining, size, and shape changes 38 . Whitney et al 39 .…”

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“…The CNN proved its capacity to create an internal representation of the two-dimensional images, which enabled the model to represent specific locations and scales of various image features, using different elements of AI, including deep-fake [49], medicine [36,50], agriculture [51] etc.…”

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“…Semantic segmentation is used to classify all pixel points in an image and assign category labels. Currently, UNET [15] and its modifications [16][17][18] are extensively utilized in semantic segmentation of microscopic medical cell images. However, a significant drawback of semantic segmentation is its inability to discern between different instances of the same category.…”