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Veluponnar D, de Boer LL, Geldof F, Jong LJS, Da Silva Guimaraes M, Vrancken Peeters MJTFD, van Duijnhoven F, Ruers T, Dashtbozorg B. Toward Intraoperative Margin Assessment Using a Deep Learning-Based Approach for Automatic Tumor Segmentation in Breast Lumpectomy Ultrasound Images. Cancers (Basel) 2023; 15:cancers15061652. [PMID: 36980539 PMCID: PMC10046373 DOI: 10.3390/cancers15061652] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/01/2023] [Accepted: 03/06/2023] [Indexed: 03/30/2023] Open
Abstract
There is an unmet clinical need for an accurate, rapid and reliable tool for margin assessment during breast-conserving surgeries. Ultrasound offers the potential for a rapid, reproducible, and non-invasive method to assess margins. However, it is challenged by certain drawbacks, including a low signal-to-noise ratio, artifacts, and the need for experience with the acquirement and interpretation of images. A possible solution might be computer-aided ultrasound evaluation. In this study, we have developed new ensemble approaches for automated breast tumor segmentation. The ensemble approaches to predict positive and close margins (distance from tumor to margin ≤ 2.0 mm) in the ultrasound images were based on 8 pre-trained deep neural networks. The best optimum ensemble approach for segmentation attained a median Dice score of 0.88 on our data set. Furthermore, utilizing the segmentation results we were able to achieve a sensitivity of 96% and a specificity of 76% for predicting a close margin when compared to histology results. The promising results demonstrate the capability of AI-based ultrasound imaging as an intraoperative surgical margin assessment tool during breast-conserving surgery.
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Affiliation(s)
- Dinusha Veluponnar
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Lisanne L de Boer
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Freija Geldof
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Lynn-Jade S Jong
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Marcos Da Silva Guimaraes
- Department of Pathology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | | | - Frederieke van Duijnhoven
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Theo Ruers
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Behdad Dashtbozorg
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
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BUSIS: A Benchmark for Breast Ultrasound Image Segmentation. Healthcare (Basel) 2022; 10:healthcare10040729. [PMID: 35455906 PMCID: PMC9025635 DOI: 10.3390/healthcare10040729] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/07/2022] [Accepted: 04/08/2022] [Indexed: 02/06/2023] Open
Abstract
Breast ultrasound (BUS) image segmentation is challenging and critical for BUS computer-aided diagnosis (CAD) systems. Many BUS segmentation approaches have been studied in the last two decades, but the performances of most approaches have been assessed using relatively small private datasets with different quantitative metrics, which results in a discrepancy in performance comparison. Therefore, there is a pressing need for building a benchmark to compare existing methods using a public dataset objectively, to determine the performance of the best breast tumor segmentation algorithm available today, and to investigate what segmentation strategies are valuable in clinical practice and theoretical study. In this work, a benchmark for B-mode breast ultrasound image segmentation is presented. In the benchmark, (1) we collected 562 breast ultrasound images and proposed standardized procedures to obtain accurate annotations using four radiologists; (2) we extensively compared the performance of 16 state-of-the-art segmentation methods and demonstrated that most deep learning-based approaches achieved high dice similarity coefficient values (DSC ≥ 0.90) and outperformed conventional approaches; (3) we proposed the losses-based approach to evaluate the sensitivity of semi-automatic segmentation to user interactions; and (4) the successful segmentation strategies and possible future improvements were discussed in details.
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Nugroho A, Hidayat R, Nugroho HA, Debayle J. Combinatorial active contour bilateral filter for ultrasound image segmentation. J Med Imaging (Bellingham) 2020; 7:057003. [PMID: 33344671 PMCID: PMC7746853 DOI: 10.1117/1.jmi.7.5.057003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2019] [Accepted: 10/07/2020] [Indexed: 11/14/2022] Open
Abstract
Purpose: Utilization of computer-aided diagnosis (CAD) on radiological ultrasound (US) imaging has increased tremendously. The prominent CAD applications are found in breast and thyroid cancer investigation. To make appropriate clinical recommendations, it is important to accurately segment the cancerous object called a lesion. Segmentation is a crucial step but undoubtedly a challenging problem due to various perturbations, e.g., speckle noise, intensity inhomogeneity, and low contrast. Approach: We present a combinatorial framework for US image segmentation using a bilateral filter (BF) and hybrid region-edge-based active contour (AC) model. The BF is adopted to smooth images while preserving edges. Then the hybrid model of region and edge-based AC is applied along the scales in a global-to-local manner to capture the lesion areas. The framework was tested in segmenting 258 US images of breast and thyroid, which were validated by manual ground truths. Results: The proposed framework is accessed quantitatively based on the overlapping values of the Dice coefficient, which reaches 90.05±5.81%. The evaluation with and without the BF shows that the enhancement procedure improves the framework well. Conclusions: The high performance of the proposed method in our experimental results indicates its potential for practical implementations in CAD radiological US systems.
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Affiliation(s)
- Anan Nugroho
- Universitas Gadjah Mada, Department of Electrical and Information Engineering, Yogyakarta, Indonesia.,Universitas Negeri Semarang, Department of Electrical Engineering, Semarang, Indonesia
| | - Risanuri Hidayat
- Universitas Gadjah Mada, Department of Electrical and Information Engineering, Yogyakarta, Indonesia
| | - Hanung A Nugroho
- Universitas Gadjah Mada, Department of Electrical and Information Engineering, Yogyakarta, Indonesia
| | - Johan Debayle
- MINES Saint-Étienne, SPIN/LGF CNRS UMR 5307, Saint-Étienne Cedex 2, France
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Vairavan R, Abdullah O, Retnasamy PB, Sauli Z, Shahimin MM, Retnasamy V. A Brief Review on Breast Carcinoma and Deliberation on Current Non Invasive Imaging Techniques for Detection. Curr Med Imaging 2020; 15:85-121. [PMID: 31975658 DOI: 10.2174/1573405613666170912115617] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Revised: 08/27/2017] [Accepted: 08/29/2017] [Indexed: 12/22/2022]
Abstract
BACKGROUND Breast carcinoma is a life threatening disease that accounts for 25.1% of all carcinoma among women worldwide. Early detection of the disease enhances the chance for survival. DISCUSSION This paper presents comprehensive report on breast carcinoma disease and its modalities available for detection and diagnosis, as it delves into the screening and detection modalities with special focus placed on the non-invasive techniques and its recent advancement work done, as well as a proposal on a novel method for the application of early breast carcinoma detection. CONCLUSION This paper aims to serve as a foundation guidance for the reader to attain bird's eye understanding on breast carcinoma disease and its current non-invasive modalities.
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Affiliation(s)
- Rajendaran Vairavan
- School of Microelectronic Engineering, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia
| | - Othman Abdullah
- Hospital Sultan Abdul Halim, 08000 Sg. Petani, Kedah, Malaysia
| | | | - Zaliman Sauli
- School of Microelectronic Engineering, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia
| | - Mukhzeer Mohamad Shahimin
- Department of Electrical and Electronic Engineering, Faculty of Engineering, National Defence University of Malaysia (UPNM), Kem Sungai Besi, 57000 Kuala Lumpur, Malaysia
| | - Vithyacharan Retnasamy
- School of Microelectronic Engineering, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia
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Wang F, Liu X, Yuan N, Qian B, Ruan L, Yin C, Jin C. Study on automatic detection and classification of breast nodule using deep convolutional neural network system. J Thorac Dis 2020; 12:4690-4701. [PMID: 33145042 PMCID: PMC7578508 DOI: 10.21037/jtd-19-3013] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Backgrounds Conventional ultrasound manual scanning and artificial diagnosis approaches in breast are considered to be operator-dependence, slight slow and error-prone. In this study, we used Automated Breast Ultrasound (ABUS) machine for the scanning, and deep convolutional neural network (CNN) technology, a kind of Deep Learning (DL) algorithm, for the detection and classification of breast nodules, aiming to achieve the automatic and accurate diagnosis of breast nodules. Methods Two hundred and ninety-three lesions from 194 patients with definite pathological diagnosis results (117 benign and 176 malignancy) were recruited as case group. Another 70 patients without breast diseases were enrolled as control group. All the breast scans were carried out by an ABUS machine and then randomly divided into training set, verification set and test set, with a proportion of 7:1:2. In the training set, we constructed a detection model by a three-dimensionally U-shaped convolutional neural network (3D U-Net) architecture for the purpose of segment the nodules from background breast images. Processes such as residual block, attention connections, and hard mining were used to optimize the model while strategies of random cropping, flipping and rotation for data augmentation. In the test phase, the current model was compared with those in previously reported studies. In the verification set, the detection effectiveness of detection model was evaluated. In the classification phase, multiple convolutional layers and fully-connected layers were applied to set up a classification model, aiming to identify whether the nodule was malignancy. Results Our detection model yielded a sensitivity of 91% and 1.92 false positive subjects per automatically scanned imaging. The classification model achieved a sensitivity of 87.0%, a specificity of 88.0% and an accuracy of 87.5%. Conclusions Deep CNN combined with ABUS maybe a promising tool for easy detection and accurate diagnosis of breast nodule.
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Affiliation(s)
- Feiqian Wang
- Department of Ultrasound, The First Affiliated Hospital of Xi'an Jiaotong University, China
| | - Xiaotong Liu
- National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, China.,School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Na Yuan
- Department of Ultrasound, The First Affiliated Hospital of Xi'an Jiaotong University, China
| | - Buyue Qian
- National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, China.,School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Litao Ruan
- Department of Ultrasound, The First Affiliated Hospital of Xi'an Jiaotong University, China
| | - Changchang Yin
- National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, China.,School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Ciping Jin
- National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, China.,School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
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Lee J, Nishikawa RM, Reiser I, Boone JM. Neutrosophic segmentation of breast lesions for dedicated breast computed tomography. J Med Imaging (Bellingham) 2018; 5:014505. [PMID: 29541650 PMCID: PMC5839418 DOI: 10.1117/1.jmi.5.1.014505] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Accepted: 02/12/2018] [Indexed: 11/14/2022] Open
Abstract
We proposed the neutrosophic approach for segmenting breast lesions in breast computed tomography (bCT) images. The neutrosophic set considers the nature and properties of neutrality (or indeterminacy). We considered the image noise as an indeterminate component while treating the breast lesion and other breast areas as true and false components. We iteratively smoothed and contrast-enhanced the image to reduce the noise level of the true set. We then applied one existing algorithm for bCT images, the RGI segmentation, on the resulting noise-reduced image to segment the breast lesions. We compared the segmentation performance of the proposed method (named as NS-RGI) to that of the regular RGI segmentation. We used 122 breast lesions (44 benign and 78 malignant) of 111 noncontrast enhanced bCT cases. We measured the segmentation performances of the NS-RGI and the RGI using the Dice coefficient. The average Dice values of the NS-RGI and RGI were 0.82 and 0.80, respectively, and their difference was statistically significant ([Formula: see text]). We conducted a subsequent feature analysis on the resulting segmentations. The classifier performance for the NS-RGI ([Formula: see text]) improved over that of the RGI ([Formula: see text], [Formula: see text]).
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Affiliation(s)
- Juhun Lee
- University of Pittsburgh, Department of Radiology, Pittsburgh, Pennsylvania, United States
| | - Robert M. Nishikawa
- University of Pittsburgh, Department of Radiology, Pittsburgh, Pennsylvania, United States
| | - Ingrid Reiser
- University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - John M. Boone
- University of California Davis Medical Center, Department of Radiology, Sacramento, California, United States
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Yi F, Huang J, Yang L, Xie Y, Xiao G. Automatic extraction of cell nuclei from H&E-stained histopathological images. J Med Imaging (Bellingham) 2017; 4:027502. [PMID: 28653017 PMCID: PMC5478972 DOI: 10.1117/1.jmi.4.2.027502] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Accepted: 05/31/2017] [Indexed: 12/15/2022] Open
Abstract
Extraction of cell nuclei from hematoxylin and eosin (H&E)-stained histopathological images is an essential preprocessing step in computerized image analysis for disease detection, diagnosis, and prognosis. We present an automated cell nuclei segmentation approach that works with H&E-stained images. A color deconvolution algorithm was first applied to the image to get the hematoxylin channel. Using a morphological operation and thresholding technique on the hematoxylin channel image, candidate target nuclei and background regions were detected, which were then used as markers for a marker-controlled watershed transform segmentation algorithm. Moreover, postprocessing was conducted to split the touching nuclei. For each segmented region from the previous steps, the regional maximum value positions were identified as potential nuclei centers. These maximum values were further grouped into [Formula: see text]-clusters, and the locations within each cluster were connected with the minimum spanning tree technique. Then, these connected positions were utilized as new markers for a watershed segmentation approach. The final number of nuclei at each region was determined by minimizing an objective function that iterated all of the possible [Formula: see text]-values. The proposed method was applied to the pathological images of the tumor tissues from The Cancer Genome Atlas study. Experimental results show that the proposed method can lead to promising results in terms of segmentation accuracy and separation of touching nuclei.
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Affiliation(s)
- Faliu Yi
- University of Texas Southwestern Medical Center, Quantitative Biomedical Research Center, Department of Clinical Science, Dallas, Texas, United States
| | - Junzhou Huang
- University of Texas at Arlington, Department of Computer Science and Engineering, Arlington, Texas, United States
| | - Lin Yang
- University of Texas Southwestern Medical Center, Quantitative Biomedical Research Center, Department of Clinical Science, Dallas, Texas, United States
- Chinese Academy of Medical Science and Peking Union Medical College, National Cancer Center/Cancer Hospital, Department of Pathology, Chaoyang District, Beijing, China
| | - Yang Xie
- University of Texas Southwestern Medical Center, Quantitative Biomedical Research Center, Department of Clinical Science, Dallas, Texas, United States
- University of Texas Southwestern Medical Center, Department of Bioinformatics, Dallas, Texas, United States
- University of Texas Southwestern Medical Center, Harold C. Simmons Comprehensive Cancer Center, Dallas, Texas, United States
| | - Guanghua Xiao
- University of Texas Southwestern Medical Center, Quantitative Biomedical Research Center, Department of Clinical Science, Dallas, Texas, United States
- University of Texas Southwestern Medical Center, Department of Bioinformatics, Dallas, Texas, United States
- University of Texas Southwestern Medical Center, Harold C. Simmons Comprehensive Cancer Center, Dallas, Texas, United States
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Araújo T, Abayazid M, Rutten MJCM, Misra S. Segmentation and three-dimensional reconstruction of lesions using the automated breast volume scanner (ABVS). Int J Med Robot 2016; 13. [DOI: 10.1002/rcs.1767] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2016] [Revised: 07/11/2016] [Accepted: 07/12/2016] [Indexed: 01/06/2023]
Affiliation(s)
- Teresa Araújo
- Department of Biomechanical Engineering; University of Twente; P. O. Box 217 7500 AE Enschede Overijsel Netherlands
- Faculty of Engineering of University of Porto; Rua Dr. Roberto Frias 4200-465 Porto Portugal
| | - Momen Abayazid
- Department of Biomechanical Engineering; University of Twente; P. O. Box 217 7500 AE Enschede Overijsel Netherlands
- Department of Radiology; Brigham and Women's Hospital and Harvard Medical School; 75 Francis Street Boston MA 02119 USA
| | - Matthieu J. C. M. Rutten
- Department of Radiology; Jeroen Bosch Hospital; Nieuwstraat 34 5211 NL's-Hertogenbosch The Netherlands
| | - Sarthak Misra
- Department of Biomechanical Engineering; University of Twente; P. O. Box 217 7500 AE Enschede Overijsel Netherlands
- Department of Biomedical Engineering; University of Groningen and University Medical Centre Groningen; Antonius Deusinglaan 1 9713 AV Groningen The Netherlands
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