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Li X, Lv S, Tong C, Qin Y, Liang C, Ma Y, Li M, Luo H, Yin S. MsgeCNN: Multiscale geometric embedded convolutional neural network for ONFH segmentation and grading. Med Phys 2023. [PMID: 36808748 DOI: 10.1002/mp.16302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 01/24/2023] [Accepted: 01/26/2023] [Indexed: 02/21/2023] Open
Abstract
BACKGROUND The incidence of osteonecrosis of the femoral head (ONFH) is increasing gradually, rapid and accurate grading of ONFH is critical. The existing Steinberg staging criteria grades ONFH according to the proportion of necrosis area to femoral head area. PURPOSE In the clinical practice, the necrosis region and femoral head region are mainly estimated by the observation and experience of doctor. This paper proposes a two-stage segmentation and grading framework, which can be used to segment the femoral head and necrosis, as well as to diagnosis. METHODS The core of the proposed two-stage framework is the multiscale geometric embedded convolutional neural network (MsgeCNN), which integrates geometric information into the training process and accurately segments the femoral head region. Then, the necrosis regions are segmented by the adaptive threshold method taking femoral head as the background. The area and proportion of the two are calculated to determine the grade. RESULTS The accuracy of the proposed MsgeCNN for femoral head segmentation is 97.73%, sensitivity is 91.17%, specificity is 99.40%, dice score is 93.34%. And the segmentation performance is better than the existing five segmentation algorithms. The diagnostic accuracy of the overall framework is 90.80%. CONCLUSIONS The proposed framework can accurately segment the femoral head region and the necrosis region. The area, proportion, and other pathological information of the framework output provide auxiliary strategies for subsequent clinical treatment.
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Affiliation(s)
- Xiang Li
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China
| | - Songcen Lv
- Department of Orthopedics, Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Chuanxin Tong
- Department of Orthopedics, Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Yong Qin
- Department of Orthopedics, Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Chen Liang
- Department of Orthopedics, Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Yingkai Ma
- Department of Orthopedics, Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Minglei Li
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China
| | - Hao Luo
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China
| | - Shen Yin
- Department of Mechanical and Industrial Engineering, Faculty of Engineering, Norwegian University of Science and Technology, Trondheim, Norway
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2
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Breast cancer detection model using fuzzy entropy segmentation and ensemble classification. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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3
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A Parallel Spiking Neural Network Based on Adaptive Lateral Inhibition Mechanism for Objective Recognition. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4242235. [DOI: 10.1155/2022/4242235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/17/2022] [Accepted: 10/03/2022] [Indexed: 11/17/2022]
Abstract
Spiking neural network (SNN) has attracted extensive attention in the field of machine learning because of its biological interpretability and low power consumption. However, the accuracy of pattern recognition cannot completely surpass deep neural networks (DNNs). The main reason is that the inherent nondifferentiability of spiking neurons makes SNN unable to be trained directly by the gradient descent algorithm, and there is also no unified training algorithm for SNN. Inspired by the biological vision system, this paper proposes a parallel convolution SNN structure combined with an adaptive lateral inhibition mechanism. And, a way of dynamically evolving the time constant with the training of SNN is proposed to ensure the diversity of neurons. This paper verifies the effectiveness of the proposed methods on static datasets and neuromorphic datasets and extends it to the recognition of breast tumors. Experimental results show that the SNN has obvious advantages in dynamical datasets. For breast tumors, it is also an edge-based task, because the edge of a medical image contains the most important information in the image. This kind of information can provide great help for the noninvasive and accurate diagnosis of diseases. The Experimental results show that the proposed method is very close to the recognition results of DNNs on static datasets, and its performance on neuromorphic datasets exceeds that of DNNs.
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A Diagnostic Model of Breast Cancer Based on Digital Mammogram Images Using Machine Learning Techniques. APPLIED COMPUTATIONAL INTELLIGENCE AND SOFT COMPUTING 2022. [DOI: 10.1155/2022/3895976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Breast cancer disease is one of the most recorded cancers that lead to morbidity and maybe death among women around the world. Recent research statistics have exposed that one from 8 females in the USA and one from 10 females in Europe are contaminated by breast cancer. The challenge with this disease is how to develop a relaxed and fast diagnosing method. One of the attractive ways of early breast cancer diagnosis is based on the mammogram images analysis of the breast using a computer-aided diagnosing (CAD) tool. This paper firstly aimed to propose an efficient method for diagnosing tumors based on mammogram images of breasts using a machine learning approach. Secondly, this paper aimed to the development of a CAD software program for breast cancer diagnosing based on the proposed method in the first step. The followed step-by-step procedure of the proposed method is performed by passing the Mammographic Image Analysis Society (MIAS) through five steps of image preprocessing, image segmentation using seeded region growing (SRG) algorithm, feature extraction using different feature’s extraction classes, and important and effectiveness feature selection using the Sequential Forward Selection (SFS) technique, and finally, the Support Vector Machine (SVM) algorithm is used as a binary classifier in two classification levels. The first level classifier is used to categorize the given image as normal or abnormal while the second-level classifier is used for further classifying the abnormal image as either a malignant or benign cancer. The proposed method is studied and investigated in two phases: the training phase and the testing phase, with the MIAS dataset of mammogram images, using 70% and 30% ratios of dataset images for the training and testing sets, respectively. The practical implementation of the proposed method and the graphical user interface (GUI) CAD tool are carried out using MATLAB software. Experimental results of the proposed method have shown that the accuracy of the proposed method reached 100% in classifying images as normal and abnormal mammogram images while the classification accuracy for benign and malignant is equal to 87.1%.
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Wimmer M, Sluiter G, Major D, Lenis D, Berg A, Neubauer T, Buhler K. Multi-Task Fusion for Improving Mammography Screening Data Classification. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:937-950. [PMID: 34788218 DOI: 10.1109/tmi.2021.3129068] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Machine learning and deep learning methods have become essential for computer-assisted prediction in medicine, with a growing number of applications also in the field of mammography. Typically these algorithms are trained for a specific task, e.g., the classification of lesions or the prediction of a mammogram's pathology status. To obtain a comprehensive view of a patient, models which were all trained for the same task(s) are subsequently ensembled or combined. In this work, we propose a pipeline approach, where we first train a set of individual, task-specific models and subsequently investigate the fusion thereof, which is in contrast to the standard model ensembling strategy. We fuse model predictions and high-level features from deep learning models with hybrid patient models to build stronger predictors on patient level. To this end, we propose a multi-branch deep learning model which efficiently fuses features across different tasks and mammograms to obtain a comprehensive patient-level prediction. We train and evaluate our full pipeline on public mammography data, i.e., DDSM and its curated version CBIS-DDSM, and report an AUC score of 0.962 for predicting the presence of any lesion and 0.791 for predicting the presence of malignant lesions on patient level. Overall, our fusion approaches improve AUC scores significantly by up to 0.04 compared to standard model ensembling. Moreover, by providing not only global patient-level predictions but also task-specific model results that are related to radiological features, our pipeline aims to closely support the reading workflow of radiologists.
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6
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Maghsoudi OH, Gastounioti A, Scott C, Pantalone L, Wu FF, Cohen EA, Winham S, Conant EF, Vachon C, Kontos D. Deep-LIBRA: An artificial-intelligence method for robust quantification of breast density with independent validation in breast cancer risk assessment. Med Image Anal 2021; 73:102138. [PMID: 34274690 PMCID: PMC8453099 DOI: 10.1016/j.media.2021.102138] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 04/29/2021] [Accepted: 06/16/2021] [Indexed: 02/06/2023]
Abstract
Breast density is an important risk factor for breast cancer that also affects the specificity and sensitivity of screening mammography. Current federal legislation mandates reporting of breast density for all women undergoing breast cancer screening. Clinically, breast density is assessed visually using the American College of Radiology Breast Imaging Reporting And Data System (BI-RADS) scale. Here, we introduce an artificial intelligence (AI) method to estimate breast density from digital mammograms. Our method leverages deep learning using two convolutional neural network architectures to accurately segment the breast area. An AI algorithm combining superpixel generation and radiomic machine learning is then applied to differentiate dense from non-dense tissue regions within the breast, from which breast density is estimated. Our method was trained and validated on a multi-racial, multi-institutional dataset of 15,661 images (4,437 women), and then tested on an independent matched case-control dataset of 6368 digital mammograms (414 cases; 1178 controls) for both breast density estimation and case-control discrimination. On the independent dataset, breast percent density (PD) estimates from Deep-LIBRA and an expert reader were strongly correlated (Spearman correlation coefficient = 0.90). Moreover, in a model adjusted for age and BMI, Deep-LIBRA yielded a higher case-control discrimination performance (area under the ROC curve, AUC = 0.612 [95% confidence interval (CI): 0.584, 0.640]) compared to four other widely-used research and commercial breast density assessment methods (AUCs = 0.528 to 0.599). Our results suggest a strong agreement of breast density estimates between Deep-LIBRA and gold-standard assessment by an expert reader, as well as improved performance in breast cancer risk assessment over state-of-the-art open-source and commercial methods.
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Affiliation(s)
- Omid Haji Maghsoudi
- Department of Radiology, University of Pennsylvania, Philadelphia, 19104, PA, USA,
| | - Aimilia Gastounioti
- Department of Radiology, University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Christopher Scott
- Department of Health Sciences Research, Mayo Clinic, Rochester, 55905, MN, USA
| | - Lauren Pantalone
- Department of Radiology, University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Fang-Fang Wu
- Department of Health Sciences Research, Mayo Clinic, Rochester, 55905, MN, USA
| | - Eric A. Cohen
- Department of Radiology, University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Stacey Winham
- Department of Health Sciences Research, Mayo Clinic, Rochester, 55905, MN, USA
| | - Emily F. Conant
- Department of Radiology, University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Celine Vachon
- Department of Health Sciences Research, Mayo Clinic, Rochester, 55905, MN, USA
| | - Despina Kontos
- Department of Radiology, University of Pennsylvania, Philadelphia, 19104, PA, USA,
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Laishram R, Rabidas R. WDO optimized detection for mammographic masses and its diagnosis: A unified CAD system. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107620] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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8
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Fan T, Wang G, Li Y, Wang Z, Wang H. A Multi-Scale Information Fusion Level Set for Breast Tumor Segmentation. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2021. [DOI: 10.1166/jmihi.2021.3635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Purpose: Mammography is considered an effective method of examination in early breast cancer screening. Massive work by distinguished researchers of breast segmentation has been proposed. However, due to the blurry boundaries of the breast tumor, the variability of its shape
and the overlap with surrounding tissue, the breast tumor’s accurate segmentation still is a challenge. Methods: In this paper, we proposed a novel level set model which based on the optimized local region driven gradient enhanced level set model (OLR-GCV) to segment tumor within
a region of interest (ROI) in a mammogram. Firstly, Noise, labels and artifacts are removed from breast images. The ROI is then obtained using the intuitionistic fuzzy C-means method. Finally, we used OLR-GCV method to accurately segment the breast tumor. The OLR-GCV model combines regional
information, enhanced edge information and optimized Laplacian of Gaussian (LOG) energy term. The regional and enhanced edge information are used to capture local, global and gradient information of breast images. The optimized Laplacian of Gaussian (LOG) energy term is introduced in the energy
functional to further optimize edge information to improve segmentation accuracy. Results: We evaluated our method on the MIAS and DDSM datasets. It yielded a Dice value of 96.86% on the former and 95.51% on the latter. Our method proposed achieves higher accuracy of segmentation than
other State-of-the-art Methods. Conclusions: Our method has better segmentation performance, and can be used in clinical practice.
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Affiliation(s)
- Tongle Fan
- College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, China
| | - Guanglei Wang
- College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, China
| | - Yan Li
- College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, China
| | - Zhongyang Wang
- Affiliated Hospital of Hebei University, Banding, Hebei 071002, China
| | - Hongrui Wang
- College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, China
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Cao H, Pu S, Tan W, Tong J. Breast mass detection in digital mammography based on anchor-free architecture. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 205:106033. [PMID: 33845408 DOI: 10.1016/j.cmpb.2021.106033] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 02/27/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Accurate detection of breast masses in mammography images is critical to diagnose early breast cancer, which can greatly improve the patients' survival rate. However, it is still a big challenge due to the heterogeneity of breast masses and the complexity of their surrounding environment. Therefore, how to develop a robust breast mass detection framework in clinical practical applications to improve patient survival is a topic that researchers need to continue to explore. METHODS To address these problems, we propose a one-stage object detection architecture, called Breast Mass Detection Network (BMassDNet), based on anchor-free and feature pyramid which makes the detection of breast masses of different sizes well adapted. We introduce a truncation normalization method and combine it with adaptive histogram equalization to enhance the contrast between the breast mass and the surrounding environment. Meanwhile, to solve the overfitting problem caused by small data size, we propose a natural deformation data augmentation method and mend the train data dynamic updating method based on the data complexity to effectively utilize the limited data. Finally, we use transfer learning to assist the training process and to improve the robustness of the model ulteriorly. RESULTS On the INbreast dataset, each image has an average of 0.495 false positives whilst the recall rate is 0.930; On the DDSM dataset, when each image has 0.599 false positives, the recall rate reaches 0.943. CONCLUSIONS The experimental results on datasets INbreast and DDSM show that the proposed BMassDNet can obtain competitive detection performance over the current top ranked methods.
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Affiliation(s)
- Haichao Cao
- Hikvision Digital Technology Company Limited, Hangzhou310051, China
| | - Shiliang Pu
- Hikvision Digital Technology Company Limited, Hangzhou310051, China.
| | - Wenming Tan
- Hikvision Digital Technology Company Limited, Hangzhou310051, China
| | - Junyan Tong
- Hikvision Digital Technology Company Limited, Hangzhou310051, China
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10
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Automatic suspicions lesions segmentation based on variable-size windows in mammography images. HEALTH AND TECHNOLOGY 2020. [DOI: 10.1007/s12553-020-00506-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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11
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Bruno A, Ardizzone E, Vitabile S, Midiri M. A Novel Solution Based on Scale Invariant Feature Transform Descriptors and Deep Learning for the Detection of Suspicious Regions in Mammogram Images. JOURNAL OF MEDICAL SIGNALS & SENSORS 2020; 10:158-173. [PMID: 33062608 PMCID: PMC7528986 DOI: 10.4103/jmss.jmss_31_19] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Revised: 10/01/2019] [Accepted: 05/06/2020] [Indexed: 12/24/2022]
Abstract
BACKGROUND Deep learning methods have become popular for their high-performance rate in the classification and detection of events in computer vision tasks. Transfer learning paradigm is widely adopted to apply pretrained convolutional neural network (CNN) on medical domains overcoming the problem of the scarcity of public datasets. Some investigations to assess transfer learning knowledge inference abilities in the context of mammogram screening and possible combinations with unsupervised techniques are in progress. METHODS We propose a novel technique for the detection of suspicious regions in mammograms that consist of the combination of two approaches based on scale invariant feature transform (SIFT) keypoints and transfer learning with pretrained CNNs such as PyramidNet and AlexNet fine-tuned on digital mammograms generated by different mammography devices. Preprocessing, feature extraction, and selection steps characterize the SIFT-based method, while the deep learning network validates the candidate suspicious regions detected by the SIFT method. RESULTS The experiments conducted on both mini-MIAS dataset and our new public dataset Suspicious Region Detection on Mammogram from PP (SuReMaPP) of 384 digital mammograms exhibit high performances compared to several state-of-the-art methods. Our solution reaches 98% of sensitivity and 90% of specificity on SuReMaPP and 94% of sensitivity and 91% of specificity on mini-MIAS. CONCLUSIONS The experimental sessions conducted so far prompt us to further investigate the powerfulness of transfer learning over different CNNs and possible combinations with unsupervised techniques. Transfer learning performances' accuracy may decrease when the training and testing images come out from mammography devices with different properties.
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Affiliation(s)
- Alessandro Bruno
- Faculty of Media and Communication, Department - NCCA (National Centre for Computer Animation) at Bournemouth University, Poole, Dorset, United Kingdom
| | | | - Salvatore Vitabile
- Department of Biomedicine, Neuroscience and Advanced Diagnostic at Palermo University, Palermo, Italy
| | - Massimo Midiri
- Department of Biomedicine, Neuroscience and Advanced Diagnostic at Palermo University, Palermo, Italy
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Wong DJ, Gandomkar Z, Wu W, Zhang G, Gao W, He X, Wang Y, Reed W. Artificial intelligence and convolution neural networks assessing mammographic images: a narrative literature review. J Med Radiat Sci 2020; 67:134-142. [PMID: 32134206 PMCID: PMC7276180 DOI: 10.1002/jmrs.385] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 01/18/2020] [Accepted: 02/11/2020] [Indexed: 11/06/2022] Open
Abstract
Studies have shown that the use of artificial intelligence can reduce errors in medical image assessment. The diagnosis of breast cancer is an essential task; however, diagnosis can include 'detection' and 'interpretation' errors. Studies to reduce these errors have shown the feasibility of using convolution neural networks (CNNs). This narrative review presents recent studies in diagnosing mammographic malignancy investigating the accuracy and reliability of these CNNs. Databases including ScienceDirect, PubMed, MEDLINE, British Medical Journal and Medscape were searched using the terms 'convolutional neural network or artificial intelligence', 'breast neoplasms [MeSH] or breast cancer or breast carcinoma' and 'mammography [MeSH Terms]'. Articles collected were screened under the inclusion and exclusion criteria, accounting for the publication date and exclusive use of mammography images, and included only literature in English. After extracting data, results were compared and discussed. This review included 33 studies and identified four recurring categories of studies: the differentiation of benign and malignant masses, the localisation of masses, cancer-containing and cancer-free breast tissue differentiation and breast classification based on breast density. CNN's application in detecting malignancy in mammography appears promising but requires further standardised investigations before potentially becoming an integral part of the diagnostic routine in mammography.
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Affiliation(s)
- Dennis Jay Wong
- Discipline of Medical Imaging SciencesThe University of SydneyLidcombeNew South WalesAustralia
| | - Ziba Gandomkar
- Discipline of Medical Imaging SciencesThe University of SydneyLidcombeNew South WalesAustralia
| | - Wan‐Jing Wu
- Discipline of Medical Imaging SciencesThe University of SydneyLidcombeNew South WalesAustralia
| | - Guijing Zhang
- Discipline of Medical Imaging SciencesThe University of SydneyLidcombeNew South WalesAustralia
| | - Wushuang Gao
- Discipline of Medical Imaging SciencesThe University of SydneyLidcombeNew South WalesAustralia
| | - Xiaoying He
- Discipline of Medical Imaging SciencesThe University of SydneyLidcombeNew South WalesAustralia
| | - Yunuo Wang
- Discipline of Medical Imaging SciencesThe University of SydneyLidcombeNew South WalesAustralia
| | - Warren Reed
- Discipline of Medical Imaging SciencesThe University of SydneyLidcombeNew South WalesAustralia
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Agarwal R, Díaz O, Yap MH, Lladó X, Martí R. Deep learning for mass detection in Full Field Digital Mammograms. Comput Biol Med 2020; 121:103774. [PMID: 32339095 DOI: 10.1016/j.compbiomed.2020.103774] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 04/19/2020] [Accepted: 04/19/2020] [Indexed: 10/24/2022]
Abstract
In recent years, the use of Convolutional Neural Networks (CNNs) in medical imaging has shown improved performance in terms of mass detection and classification compared to current state-of-the-art methods. This paper proposes a fully automated framework to detect masses in Full-Field Digital Mammograms (FFDM). This is based on the Faster Region-based Convolutional Neural Network (Faster-RCNN) model and is applied for detecting masses in the large-scale OPTIMAM Mammography Image Database (OMI-DB), which consists of ∼80,000 FFDMs mainly from Hologic and General Electric (GE) scanners. This research is the first to benchmark the performance of deep learning on OMI-DB. The proposed framework obtained a True Positive Rate (TPR) of 0.93 at 0.78 False Positive per Image (FPI) on FFDMs from the Hologic scanner. Transfer learning is then used in the Faster R-CNN model trained on Hologic images to detect masses in smaller databases containing FFDMs from the GE scanner and another public dataset INbreast (Siemens scanner). The detection framework obtained a TPR of 0.91±0.06 at 1.69 FPI for images from the GE scanner and also showed higher performance compared to state-of-the-art methods on the INbreast dataset, obtaining a TPR of 0.99±0.03 at 1.17 FPI for malignant and 0.85±0.08 at 1.0 FPI for benign masses, showing the potential to be used as part of an advanced CAD system for breast cancer screening.
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Affiliation(s)
- Richa Agarwal
- VICOROB, Department of Computer Architecture and Technology, University of Girona, Spain.
| | - Oliver Díaz
- VICOROB, Department of Computer Architecture and Technology, University of Girona, Spain; Department of Mathematics and Computer Science, University of Barcelona, Spain.
| | - Moi Hoon Yap
- Department of Computing and Mathematics, Manchester Metropolitan University, United Kingdom.
| | - Xavier Lladó
- VICOROB, Department of Computer Architecture and Technology, University of Girona, Spain.
| | - Robert Martí
- VICOROB, Department of Computer Architecture and Technology, University of Girona, Spain.
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Rundo L, Tangherloni A, Cazzaniga P, Nobile MS, Russo G, Gilardi MC, Vitabile S, Mauri G, Besozzi D, Militello C. A novel framework for MR image segmentation and quantification by using MedGA. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 176:159-172. [PMID: 31200903 DOI: 10.1016/j.cmpb.2019.04.016] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 04/14/2019] [Accepted: 04/16/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVES Image segmentation represents one of the most challenging issues in medical image analysis to distinguish among different adjacent tissues in a body part. In this context, appropriate image pre-processing tools can improve the result accuracy achieved by computer-assisted segmentation methods. Taking into consideration images with a bimodal intensity distribution, image binarization can be used to classify the input pictorial data into two classes, given a threshold intensity value. Unfortunately, adaptive thresholding techniques for two-class segmentation work properly only for images characterized by bimodal histograms. We aim at overcoming these limitations and automatically determining a suitable optimal threshold for bimodal Magnetic Resonance (MR) images, by designing an intelligent image analysis framework tailored to effectively assist the physicians during their decision-making tasks. METHODS In this work, we present a novel evolutionary framework for image enhancement, automatic global thresholding, and segmentation, which is here applied to different clinical scenarios involving bimodal MR image analysis: (i) uterine fibroid segmentation in MR guided Focused Ultrasound Surgery, and (ii) brain metastatic cancer segmentation in neuro-radiosurgery therapy. Our framework exploits MedGA as a pre-processing stage. MedGA is an image enhancement method based on Genetic Algorithms that improves the threshold selection, obtained by the efficient Iterative Optimal Threshold Selection algorithm, between the underlying sub-distributions in a nearly bimodal histogram. RESULTS The results achieved by the proposed evolutionary framework were quantitatively evaluated, showing that the use of MedGA as a pre-processing stage outperforms the conventional image enhancement methods (i.e., histogram equalization, bi-histogram equalization, Gamma transformation, and sigmoid transformation), in terms of both MR image enhancement and segmentation evaluation metrics. CONCLUSIONS Thanks to this framework, MR image segmentation accuracy is considerably increased, allowing for measurement repeatability in clinical workflows. The proposed computational solution could be well-suited for other clinical contexts requiring MR image analysis and segmentation, aiming at providing useful insights for differential diagnosis and prognosis.
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Affiliation(s)
- Leonardo Rundo
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy; Institute of Molecular Bioimaging and Physiology, Italian National Research Council, Cefalù, PA, Italy; Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, Cambridge, UK.
| | - Andrea Tangherloni
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy; Department of Haematology, University of Cambridge, Cambridge, UK; Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK.
| | - Paolo Cazzaniga
- Department of Human and Social Sciences, University of Bergamo, Bergamo, Italy; SYSBIO.IT Centre of Systems Biology, Milan, Italy.
| | - Marco S Nobile
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy; SYSBIO.IT Centre of Systems Biology, Milan, Italy.
| | - Giorgio Russo
- Institute of Molecular Bioimaging and Physiology, Italian National Research Council, Cefalù, PA, Italy.
| | - Maria Carla Gilardi
- Institute of Molecular Bioimaging and Physiology, Italian National Research Council, Cefalù, PA, Italy.
| | - Salvatore Vitabile
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy.
| | - Giancarlo Mauri
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy; SYSBIO.IT Centre of Systems Biology, Milan, Italy.
| | - Daniela Besozzi
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.
| | - Carmelo Militello
- Institute of Molecular Bioimaging and Physiology, Italian National Research Council, Cefalù, PA, Italy.
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15
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Adaptive hysteresis thresholding segmentation technique for localizing the breast masses in the curve stitching domain. Int J Med Inform 2019; 126:26-34. [DOI: 10.1016/j.ijmedinf.2019.02.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 12/10/2018] [Accepted: 02/03/2019] [Indexed: 02/03/2023]
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Maisen C, Auephanwiriyakul S, Theera-Umpon N. Learning vector quantization inference classifier in breast abnormality classification. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018. [DOI: 10.3233/jifs-169850] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Chakkraphop Maisen
- Computer Engineering Department, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand
- Graduate School, Chiang Mai University, Chiang Mai, Thailand
| | - Sansanee Auephanwiriyakul
- Computer Engineering Department, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand
- Biomedical Engineering Institute, Chiang Mai University, Chiang Mai, Thailand
| | - Nipon Theera-Umpon
- Electrical Engineering Department, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand
- Biomedical Engineering Institute, Chiang Mai University, Chiang Mai, Thailand
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Hmida M, Hamrouni K, Solaiman B, Boussetta S. Mammographic mass segmentation using fuzzy contours. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 164:131-142. [PMID: 30195421 DOI: 10.1016/j.cmpb.2018.07.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Revised: 06/15/2018] [Accepted: 07/16/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Accurate mass segmentation in mammographic images is a critical requirement for computer-aided diagnosis systems since it allows accurate feature extraction and thus improves classification precision. METHODS In this paper, a novel automatic breast mass segmentation approach is presented. This approach consists of mainly three stages: contour initialization applied to a given region of interest; construction of fuzzy contours and estimation of fuzzy membership maps of different classes in the considered image; integration of these maps in the Chan-Vese model to get a fuzzy-energy based model that is used for final delineation of mass. RESULTS The proposed approach is evaluated using mass regions of interest extracted from the mini-MIAS database. The experimental results show that the proposed method achieves an average true positive rate of 91.12% with a precision of 88.08%. CONCLUSIONS The achieved results show high accuracy in breast mass segmentation when compared to manually annotated ground truth and to other methods from the literature.
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Affiliation(s)
- Marwa Hmida
- Université de Tunis El Manar, Ecole Nationale d'Ingnieurs de Tunis, LR-Signal Image et Technologies de l'Information, Tunis 1002, Tunisie; IMT Atlantique, ITI Laboratory, Brest 29238, France.
| | - Kamel Hamrouni
- Université de Tunis El Manar, Ecole Nationale d'Ingnieurs de Tunis, LR-Signal Image et Technologies de l'Information, Tunis 1002, Tunisie.
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Sapate SG, Mahajan A, Talbar SN, Sable N, Desai S, Thakur M. Radiomics based detection and characterization of suspicious lesions on full field digital mammograms. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 163:1-20. [PMID: 30119844 DOI: 10.1016/j.cmpb.2018.05.017] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Revised: 05/11/2018] [Accepted: 05/15/2018] [Indexed: 02/05/2023]
Abstract
BACKGROUND AND OBJECTIVE Early detection is the important key to reduce breast cancer mortality rate. Detecting the mammographic abnormality as a subtle sign of breast cancer is essential for the proper diagnosis and treatment. The aim of this preliminary study is to develop algorithms which detect suspicious lesions and characterize them to reduce the diagnostic errors regarding false positives and false negatives. METHODS The proposed hybrid mechanism detects suspicious lesions automatically using connected component labeling and adaptive fuzzy region growing algorithm. A novel neighboring pixel selection algorithm reduces the computational complexity of the seeded region growing algorithm used to finalize lesion contours. These lesions are characterized using radiomic features and then classified as benign mass or malignant tumor using k-NN and SVM classifiers. Two datasets of 460 full field digital mammograms (FFDM) utilized in this clinical study consists of 210 images with malignant tumors, 30 with benign masses and 220 normal breast images that are validated by radiologists expert in mammography. RESULTS The qualitative assessment of segmentation results by the expert radiologists shows 91.67% sensitivity and 58.33% specificity. The effects of seven geometric and 48 textural features on classification accuracy, false positives per image (FPsI), sensitivity and specificity are studied separately and together. The features together achieved the sensitivity of 84.44% and 85.56%, specificity of 91.11% and 91.67% with FPsI of 0.54 and 0.55 using k-NN and SVM classifiers respectively on local dataset. CONCLUSIONS The overall breast cancer detection performance of proposed scheme after combining geometric and textural features with both classifiers is improved in terms of sensitivity, specificity, and FPsI.
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Affiliation(s)
- Suhas G Sapate
- Centre of Excellence in Signal & Image Processing, SGGS Institute of Engineering & Technology, Nanded, Maharashtra, India; Department of CSE, Ashokrao Mane Group of Institutions, Vathar, Kolhapur, Maharashtra, India.
| | - Abhishek Mahajan
- Department of Radiodiagnosis, Tata Memorial Centre, Parel, Mumbai, Maharashtra, India
| | - Sanjay N Talbar
- Centre of Excellence in Signal & Image Processing, SGGS Institute of Engineering & Technology, Nanded, Maharashtra, India; Department of E&TC, SGGS Institute of Engineering & Technology, Nanded, Maharashtra, India
| | - Nilesh Sable
- Department of Radiodiagnosis, Tata Memorial Centre, Parel, Mumbai, Maharashtra, India
| | - Subhash Desai
- Department of Radiodiagnosis, Tata Memorial Centre, Parel, Mumbai, Maharashtra, India
| | - Meenakshi Thakur
- Department of Radiodiagnosis, Tata Memorial Centre, Parel, Mumbai, Maharashtra, India
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A cost-sensitive Bayesian combiner for reducing false positives in mammographic mass detection. BIOMED ENG-BIOMED TE 2017; 64:39-52. [DOI: 10.1515/bmt-2017-0032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Accepted: 09/20/2017] [Indexed: 11/15/2022]
Abstract
AbstractMammography is the most widely used modality for early breast cancer detection. This work proposes a new computer-aided mass detection approach, in which a denoising method called BM3D is first applied to mammograms. Afterwards, using an adaptive segmentation algorithm, images are segmented to suspicious regions of interest (ROIs) and then a classifier is used to understand the features of true positive (TP) and false positive (FP) patterns. In this way, from selected suspicious ROIs, fractal dimension, texture and intensity features are extracted. Subsequently, a discretization approach followed by correlation-based feature selection (CFS) is combined with a genetic algorithm to obtain the most representative features. To neutralize the classifier’s bias in favor of the major class in imbalanced datasets, an oversampling algorithm is used. In the next step, a cost-sensitive ensemble classifier based on a trainable combiner is proposed in order to reduce the number of FP samples. Finally, the presented method is validated on miniMIAS and INBreast datasets. The free-response receiver operating characteristic (FROC) analysis results prove the efficiency of the proposed approach. A sensitivity of 88% and false positive per image (FPpI) of 0.78 for miniMIAS and also a sensitivity of 86% and FPpI of 0.75 for INBreast dataset were obtained.
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