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Liu J, Sun X, Li R, Peng Y. Recognition of cervical precancerous lesions based on probability distribution feature guidance. Curr Med Imaging 2022; 18:1204-1213. [DOI: 10.2174/1573405618666220428104541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 03/07/2022] [Accepted: 03/13/2022] [Indexed: 11/22/2022]
Abstract
INTRODUCTION:
Cervical cancer is a high incidence of cancer in women and cervical precancerous screening plays an important role in reducing the mortality rate.
METHOD:
- In this study, we proposed a multichannel feature extraction method based on the probability distribution features of the acetowhite (AW) region to identify cervical precancerous lesions, with the overarching goal to improve the accuracy of cervical precancerous screening. A k-means clustering algorithm was first used to extract the cervical region images from the original colposcopy images. We then used a deep learning model called DeepLab V3+ to segment the AW region of the cervical image after the acetic acid experiment, from which the probability distribution map of the AW region after segmentation was obtained. This probability distribution map was fed into a neural network classification model for multichannel feature extraction, which resulted in the final classification performance.
RESULT:
Results of the experimental evaluation showed that the proposed method achieved an average accuracy of 87.7%, an average sensitivity of 89.3%, and an average specificity of 85.6%. Compared with the methods that did not add segmented probability features, the proposed method increased the average accuracy rate, sensitivity, and specificity by 8.3%, 8%, and 8.4%, respectively.
CONCLUSION:
Overall, the proposed method holds great promise for enhancing the screening of cervical precancerous lesions in the clinic by providing the physician with more reliable screening results that might reduce their workload.
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Affiliation(s)
- Jun Liu
- College of Information Engineering, Nanchang Hangkong University, Nanchang, Jiangxi, China
| | - Xiaoxue Sun
- College of Information Engineering, Nanchang Hangkong University, Nanchang, Jiangxi, China
| | - Rihui Li
- Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Yuanxiu Peng
- College of Information Engineering, Nanchang Hangkong University, Nanchang, Jiangxi, China
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Peng G, Dong H, Liang T, Li L, Liu J. Diagnosis of cervical precancerous lesions based on multimodal feature changes. Comput Biol Med 2021; 130:104209. [PMID: 33440316 DOI: 10.1016/j.compbiomed.2021.104209] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 12/11/2020] [Accepted: 12/31/2020] [Indexed: 12/24/2022]
Abstract
To realize the automatic diagnosis of cervical intraepithelial neoplasia (CIN) cases by preacetic acid test and postacetic acid test colposcopy images, this paper proposes a method of cervical precancerous lesion diagnosis based on multimodal feature changes. First, the preacetic acid test and postacetic acid test colposcopy images were registered based on cross-correlation and projection transformation, and then the cervical region was extracted by the k-means clustering algorithm. Finally, a deep learning network was used to extract features and classify the preacetic acid test and postacetic acid test cervical images after registration. Finally, the proposed method achieves a classification accuracy of 86.3%, a sensitivity of 84.1%, and a specificity of 89.8% in 60 test cases. Experimental results show that this method can make better use of the multimodal features of colposcopy images and has lower requirements for medical staff in the process of data acquisition. It has certain clinical significance in cervical cancer precancerous lesion screening systems.
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Affiliation(s)
- Gengyou Peng
- College of Information Engineering, Nanchang Hangkong University, Nanchang, China
| | - Hua Dong
- College of Information Engineering, Nanchang Hangkong University, Nanchang, China
| | - Tong Liang
- College of Information Engineering, Nanchang Hangkong University, Nanchang, China
| | - Ling Li
- Department of Gynecologic Oncology, Jiangxi Maternal and Child Health Hospital, Nanchang, Jiangxi, China
| | - Jun Liu
- College of Information Engineering, Nanchang Hangkong University, Nanchang, China.
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Yang B, Wu Y, Zhou Z, Li S, Qin G, Chen L, Wang J. A collection input based support tensor machine for lesion malignancy classification in digital breast tomosynthesis. Phys Med Biol 2019; 64:235007. [PMID: 31698349 PMCID: PMC7103089 DOI: 10.1088/1361-6560/ab553d] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Digital breast tomosynthesis (DBT) with improved lesion conspicuity and characterization has been adopted in screening practice. DBT-based diagnosis strongly depends on physicians' experience, so an automatic lesion malignancy classification model using DBT could improve the consistency of diagnosis among different physicians. Tensor-based approaches that use the original imaging data as input have shown promising results for many classification tasks. However, DBT data are pseudo-3D volumetric imaging as the slice spacing of DBT is much coarser than that of the in-plane resolution. Thus, directly constructing DBT as the third-order tensor in a conventional tensor-based classifier with introducing additional information to the original DBT data along the slice-spacing dimension will lead to inconsistency across all three dimensions. To avoid such inconsistency, we introduce a collection input based support tensor machine (CISTM)-based classifier that uses the tensor collection as input for classifying lesion malignancy in DBT. In CISTM, instead of introducing the third dimension directly into the geometry construction, the third-dimension structural relationship is related by weight parameters in the decision function, which is dynamically and automatically constructed during the classifier training process and is more consistent with the pseudo-3D nature of DBT. We tested our method on a DBT dataset of 926 images among which 262 were malignant and 664 were benign. We compared our method with the latest tensor-based method, KSTM (kernelled support tensor machine), which does not consider the unique non-uniform resolution property of DBT. Experimental results illustrate that the CISTM-based classifier is effective for classifying breast lesion malignancy in DBT and that it outperforms the KSTM-based classifier.
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Affiliation(s)
- Benjuan Yang
- School of Mathematics and Sciences, Guizhou Normal University, Guiyang, 50001, PR China
- Medical Artificial Intelligence and Automation (MAIA) Lab, University of Texas Southwestern Medical Center, Dallas, TX 75390, US
| | - Yingjiang Wu
- School of Information Engineering, Guangdong Medical University, Dongguan, 523808, PR China
| | - Zhiguo Zhou
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75235, US
- Medical Artificial Intelligence and Automation (MAIA) Lab, University of Texas Southwestern Medical Center, Dallas, TX 75390, US
| | - Shulong Li
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, PR China
| | - Genggeng Qin
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, PR China
| | - Liyuan Chen
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75235, US
- Medical Artificial Intelligence and Automation (MAIA) Lab, University of Texas Southwestern Medical Center, Dallas, TX 75390, US
| | - Jing Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75235, US
- Medical Artificial Intelligence and Automation (MAIA) Lab, University of Texas Southwestern Medical Center, Dallas, TX 75390, US
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Al-Dulaimi K, Tomeo-Reyes I, Banks J, Chandran V. Evaluation and benchmarking of level set-based three forces via geometric active contours for segmentation of white blood cell nuclei shape. Comput Biol Med 2019; 116:103568. [PMID: 32001010 DOI: 10.1016/j.compbiomed.2019.103568] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2019] [Revised: 11/26/2019] [Accepted: 11/26/2019] [Indexed: 01/27/2023]
Abstract
The segmentation of white blood cells and their nuclei is still difficult and challenging for many reasons, including the differences in their colour, shape, background and staining techniques, the overlapping of cells, and changing cell topologies. This paper shows how these challenges can be addressed by using level set forces via edge-based geometric active contours. In this work, three level set forces-based (curvature, normal direction, and vector field) are comprehensively studied in the context of the problem of segmenting white blood cell nuclei based on geometric flows. Cell images are first pre-processed, using contrast stretching and morphological opening and closing in order to standardise the image colour intensity, to create an initial estimate of the cell foreground and to remove the narrow links between lobes and cell bulges. Next, segmentation is conducted to prune out the white blood cell nucleus region from the cell wall and cytoplasm by combining the theory of curve evolution using curvature, normal direction, and vector field-based level set forces and edge-based geometric active contours. The overall performance of the proposed segmentation method is compared and benchmarked against existing techniques for nucleus shape detection, using the same databases. The three level set forces studied here (curvature, normal direction, and vector field) via edge-based geometric active contours achieve F-index values of 92.09%, 91.13%, and 90.76%, respectively, and the proposed segmentation method results in better performance than all other techniques for all indices, including Jaccard distance, boundary displacement error, and Rand index.
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Affiliation(s)
- Khamael Al-Dulaimi
- School of Electrical Engineering and Computer Science, Queensland University of Technology, QLD, Australia; Al-Nahrain University, Computer Science Department, Iraq.
| | - Inmaculada Tomeo-Reyes
- School of Electrical Engineering and Telecommunications, University of New South Wales, NSW, Australia.
| | - Jasmine Banks
- School of Electrical Engineering and Computer Science, Queensland University of Technology, QLD, Australia.
| | - Vinod Chandran
- School of Electrical Engineering and Computer Science, Queensland University of Technology, QLD, Australia.
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Chen L, Shen C, Zhou Z, Maquilan G, Albuquerque K, Folkert MR, Wang J. Automatic PET cervical tumor segmentation by combining deep learning and anatomic prior. Phys Med Biol 2019; 64:085019. [PMID: 30818303 DOI: 10.1088/1361-6560/ab0b64] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Cervical tumor segmentation on 3D 18FDG PET images is a challenging task because of the proximity between cervix and bladder, both of which can uptake 18FDG tracers. This problem makes traditional segmentation based on intensity variation methods ineffective and reduces overall accuracy. Based on anatomy knowledge, including 'roundness' of the cervical tumor and relative positioning between the bladder and cervix, we propose a supervised machine learning method that integrates convolutional neural network (CNN) with this prior information to segment cervical tumors. First, we constructed a spatial information embedded CNN model (S-CNN) that maps the PET image to its corresponding label map, in which bladder, other normal tissue, and cervical tumor pixels are labeled as -1, 0, and 1, respectively. Then, we obtained the final segmentation from the output of the network by a prior information constrained (PIC) thresholding method. We evaluated the performance of the PIC-S-CNN method on PET images from 50 cervical cancer patients. The PIC-S-CNN method achieved a mean Dice similarity coefficient (DSC) of 0.84 while region-growing, Chan-Vese, graph-cut, fully convolutional neural networks (FCN) based FCN-8 stride, and FCN-2 stride, and U-net achieved 0.55, 0.64, 0.67, 0.71, 0.77, and 0.80 mean DSC, respectively. The proposed PIC-S-CNN provides a more accurate way for segmenting cervical tumors on 3D PET images. Our results suggest that combining deep learning and anatomic prior information may improve segmentation accuracy for cervical tumors.
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Affiliation(s)
- Liyuan Chen
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75287, United States of America
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