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Li L, Tan J, Yu L, Li C, Nan H, Zheng S. LSAM: L2-norm self-attention and latent space feature interaction for automatic 3D multi-modal head and neck tumor segmentation. Phys Med Biol 2023; 68:225004. [PMID: 37852283 DOI: 10.1088/1361-6560/ad04a8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 10/18/2023] [Indexed: 10/20/2023]
Abstract
Objective.Head and neck (H&N) cancers are prevalent globally, and early and accurate detection is absolutely crucial for timely and effective treatment. However, the segmentation of H&N tumors is challenging due to the similar density of the tumors and surrounding tissues in CT images. While positron emission computed tomography (PET) images provide information about the metabolic activity of the tissue and can distinguish between lesion regions and normal tissue. But they are limited by their low spatial resolution. To fully leverage the complementary information from PET and CT images, we propose a novel and innovative multi-modal tumor segmentation method specifically designed for H&N tumor segmentation.Approach.The proposed novel and innovative multi-modal tumor segmentation network (LSAM) consists of two key learning modules, namely L2-Norm self-attention and latent space feature interaction, which exploit the high sensitivity of PET images and the anatomical information of CT images. These two advanced modules contribute to a powerful 3D segmentation network based on a U-shaped structure. The well-designed segmentation method can integrate complementary features from different modalities at multiple scales, thereby improving the feature interaction between modalities.Main results.We evaluated the proposed method on the public HECKTOR PET-CT dataset, and the experimental results demonstrate that the proposed method convincingly outperforms existing H&N tumor segmentation methods in terms of key evaluation metrics, including DSC (0.8457), Jaccard (0.7756), RVD (0.0938), and HD95 (11.75).Significance.The innovative Self-Attention mechanism based on L2-Norm offers scalability and is effective in reducing the impact of outliers on the performance of the model. And the novel method for multi-scale feature interaction based on Latent Space utilizes the learning process in the encoder phase to achieve the best complementary effects among different modalities.
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Affiliation(s)
- Laquan Li
- College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, People's Republic of China
- School of Science, Chongqing University of Posts and Telecommunications, Chongqing, People's Republic of China
| | - Jiaxin Tan
- College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, People's Republic of China
| | - Lei Yu
- Emergency Department, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
| | - Chunwen Li
- Emergency Department, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
| | - Hai Nan
- College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, People's Republic of China
| | - Shenhai Zheng
- College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, People's Republic of China
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Systematic Review of Tumor Segmentation Strategies for Bone Metastases. Cancers (Basel) 2023; 15:cancers15061750. [PMID: 36980636 PMCID: PMC10046265 DOI: 10.3390/cancers15061750] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/09/2023] [Accepted: 03/10/2023] [Indexed: 03/18/2023] Open
Abstract
Purpose: To investigate the segmentation approaches for bone metastases in differentiating benign from malignant bone lesions and characterizing malignant bone lesions. Method: The literature search was conducted in Scopus, PubMed, IEEE and MedLine, and Web of Science electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 77 original articles, 24 review articles, and 1 comparison paper published between January 2010 and March 2022 were included in the review. Results: The results showed that most studies used neural network-based approaches (58.44%) and CT-based imaging (50.65%) out of 77 original articles. However, the review highlights the lack of a gold standard for tumor boundaries and the need for manual correction of the segmentation output, which largely explains the absence of clinical translation studies. Moreover, only 19 studies (24.67%) specifically mentioned the feasibility of their proposed methods for use in clinical practice. Conclusion: Development of tumor segmentation techniques that combine anatomical information and metabolic activities is encouraging despite not having an optimal tumor segmentation method for all applications or can compensate for all the difficulties built into data limitations.
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Zhang X, Zhang B, Deng S, Meng Q, Chen X, Xiang D. Cross modality fusion for modality-specific lung tumor segmentation in PET-CT images. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac994e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 10/11/2022] [Indexed: 11/09/2022]
Abstract
Abstract
Although positron emission tomography-computed tomography (PET-CT) images have been widely used, it is still challenging to accurately segment the lung tumor. The respiration, movement and imaging modality lead to large modality discrepancy of the lung tumors between PET images and CT images. To overcome these difficulties, a novel network is designed to simultaneously obtain the corresponding lung tumors of PET images and CT images. The proposed network can fuse the complementary information and preserve modality-specific features of PET images and CT images. Due to the complementarity between PET images and CT images, the two modality images should be fused for automatic lung tumor segmentation. Therefore, cross modality decoding blocks are designed to extract modality-specific features of PET images and CT images with the constraints of the other modality. The edge consistency loss is also designed to solve the problem of blurred boundaries of PET images and CT images. The proposed method is tested on 126 PET-CT images with non-small cell lung cancer, and Dice similarity coefficient scores of lung tumor segmentation reach 75.66 ± 19.42 in CT images and 79.85 ± 16.76 in PET images, respectively. Extensive comparisons with state-of-the-art lung tumor segmentation methods have also been performed to demonstrate the superiority of the proposed network.
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Positron Emission Tomography Image Segmentation Based on Atanassov’s Intuitionistic Fuzzy Sets. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12104865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
In this paper, we present an approach to fully automate tumor delineation in positron emission tomography (PET) images. PET images play a major role in medicine for in vivo imaging in oncology (PET images are used to evaluate oncology patients, detecting emitted photons from a radiotracer localized in abnormal cells). PET image tumor delineation plays a vital role both in pre- and post-treatment stages. The low spatial resolution and high noise characteristics of PET images increase the challenge in PET image segmentation. Despite the difficulties and known limitations, several image segmentation approaches have been proposed. This paper introduces a new unsupervised approach to perform tumor delineation in PET images using Atanassov’s intuitionistic fuzzy sets (A-IFSs) and restricted dissimilarity functions. Moreover, the implementation of this methodology is presented and tested against other existing methodologies. The proposed algorithm increases the accuracy of tumor delineation in PET images, and the experimental results show that the proposed method outperformed all methods tested.
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Xue Z, Li P, Zhang L, Lu X, Zhu G, Shen P, Ali Shah SA, Bennamoun M. Multi-Modal Co-Learning for Liver Lesion Segmentation on PET-CT Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3531-3542. [PMID: 34133275 DOI: 10.1109/tmi.2021.3089702] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Liver lesion segmentation is an essential process to assist doctors in hepatocellular carcinoma diagnosis and treatment planning. Multi-modal positron emission tomography and computed tomography (PET-CT) scans are widely utilized due to their complementary feature information for this purpose. However, current methods ignore the interaction of information across the two modalities during feature extraction, omit the co-learning of the feature maps of different resolutions, and do not ensure that shallow and deep features complement each others sufficiently. In this paper, our proposed model can achieve feature interaction across multi-modal channels by sharing the down-sampling blocks between two encoding branches to eliminate misleading features. Furthermore, we combine feature maps of different resolutions to derive spatially varying fusion maps and enhance the lesions information. In addition, we introduce a similarity loss function for consistency constraint in case that predictions of separated refactoring branches for the same regions vary a lot. We evaluate our model for liver tumor segmentation using a PET-CT scans dataset, compare our method with the baseline techniques for multi-modal (multi-branches, multi-channels and cascaded networks) and then demonstrate that our method has a significantly higher accuracy ( ) than the baseline models.
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Montgomery MK, David J, Zhang H, Ram S, Deng S, Premkumar V, Manzuk L, Jiang ZK, Giddabasappa A. Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography. PLoS One 2021; 16:e0252950. [PMID: 34138905 PMCID: PMC8211241 DOI: 10.1371/journal.pone.0252950] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 05/25/2021] [Indexed: 12/14/2022] Open
Abstract
Unlike the majority of cancers, survival for lung cancer has not shown much improvement since the early 1970s and survival rates remain low. Genetically engineered mice tumor models are of high translational relevance as we can generate tissue specific mutations which are observed in lung cancer patients. Since these tumors cannot be detected and quantified by traditional methods, we use micro-computed tomography imaging for longitudinal evaluation and to measure response to therapy. Conventionally, we analyze microCT images of lung cancer via a manual segmentation. Manual segmentation is time-consuming and sensitive to intra- and inter-analyst variation. To overcome the limitations of manual segmentation, we set out to develop a fully-automated alternative, the Mouse Lung Automated Segmentation Tool (MLAST). MLAST locates the thoracic region of interest, thresholds and categorizes the lung field into three tissue categories: soft tissue, intermediate, and lung. An increase in the tumor burden was measured by a decrease in lung volume with a simultaneous increase in soft and intermediate tissue quantities. MLAST segmentation was validated against three methods: manual scoring, manual segmentation, and histology. MLAST was applied in an efficacy trial using a Kras/Lkb1 non-small cell lung cancer model and demonstrated adequate precision and sensitivity in quantifying tumor growth inhibition after drug treatment. Implementation of MLAST has considerably accelerated the microCT data analysis, allowing for larger study sizes and mid-study readouts. This study illustrates how automated image analysis tools for large datasets can be used in preclinical imaging to deliver high throughput and quantitative results.
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Affiliation(s)
| | - John David
- Comparative Medicine, Pfizer Inc., La Jolla, CA, United States of America
| | - Haikuo Zhang
- Oncology Research Unit, Pfizer Inc., La Jolla, CA, United States of America
| | - Sripad Ram
- Drug Safety Research Unit, Pfizer Inc., La Jolla, CA, United States of America
| | - Shibing Deng
- Early Clinical Development, Pfizer Inc., La Jolla, CA, United States of America
| | - Vidya Premkumar
- Comparative Medicine, Pfizer Inc., La Jolla, CA, United States of America
| | - Lisa Manzuk
- Comparative Medicine, Pfizer Inc., La Jolla, CA, United States of America
| | - Ziyue Karen Jiang
- Comparative Medicine, Pfizer Inc., La Jolla, CA, United States of America
| | - Anand Giddabasappa
- Comparative Medicine, Pfizer Inc., La Jolla, CA, United States of America
- * E-mail:
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Borrelli P, Ly J, Kaboteh R, Ulén J, Enqvist O, Trägårdh E, Edenbrandt L. AI-based detection of lung lesions in [ 18F]FDG PET-CT from lung cancer patients. EJNMMI Phys 2021; 8:32. [PMID: 33768311 PMCID: PMC7994489 DOI: 10.1186/s40658-021-00376-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 03/05/2021] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND [18F]-fluorodeoxyglucose (FDG) positron emission tomography with computed tomography (PET-CT) is a well-established modality in the work-up of patients with suspected or confirmed diagnosis of lung cancer. Recent research efforts have focused on extracting theragnostic and textural information from manually indicated lung lesions. Both semi-automatic and fully automatic use of artificial intelligence (AI) to localise and classify FDG-avid foci has been demonstrated. To fully harness AI's usefulness, we have developed a method which both automatically detects abnormal lung lesions and calculates the total lesion glycolysis (TLG) on FDG PET-CT. METHODS One hundred twelve patients (59 females and 53 males) who underwent FDG PET-CT due to suspected or for the management of known lung cancer were studied retrospectively. These patients were divided into a training group (59%; n = 66), a validation group (20.5%; n = 23) and a test group (20.5%; n = 23). A nuclear medicine physician manually segmented abnormal lung lesions with increased FDG-uptake in all PET-CT studies. The AI-based method was trained to segment the lesions based on the manual segmentations. TLG was then calculated from manual and AI-based measurements, respectively and analysed with Bland-Altman plots. RESULTS The AI-tool's performance in detecting lesions had a sensitivity of 90%. One small lesion was missed in two patients, respectively, where both had a larger lesion which was correctly detected. The positive and negative predictive values were 88% and 100%, respectively. The correlation between manual and AI TLG measurements was strong (R2 = 0.74). Bias was 42 g and 95% limits of agreement ranged from - 736 to 819 g. Agreement was particularly high in smaller lesions. CONCLUSIONS The AI-based method is suitable for the detection of lung lesions and automatic calculation of TLG in small- to medium-sized tumours. In a clinical setting, it will have an added value due to its capability to sort out negative examinations resulting in prioritised and focused care on patients with potentially malignant lesions.
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Affiliation(s)
- Pablo Borrelli
- Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - John Ly
- Department of Radiology, Kristianstad Hospital, Kristianstad, Sweden. .,Department of Translational Medicine and Wallenberg Center for Molecular Medicine, Lund University, Malmö, Sweden.
| | - Reza Kaboteh
- Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | | | - Olof Enqvist
- Eigenvision AB, Malmö, Sweden.,Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Elin Trägårdh
- Department of Translational Medicine and Wallenberg Center for Molecular Medicine, Lund University, Malmö, Sweden.,Department of Clinical Physiology and Nuclear Medicine, Skåne University Hospital, Malmö, Sweden
| | - Lars Edenbrandt
- Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden.,Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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Kim YJ, Lee SH, Lim KY, Kim KG. Development and Validation of Segmentation Method for Lung Cancer Volumetry on Chest CT. J Digit Imaging 2018; 31:505-512. [PMID: 29380154 PMCID: PMC6113144 DOI: 10.1007/s10278-018-0051-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
The set of criteria called Response Evaluation Criteria In Solid Tumors (RECIST) is used to evaluate the remedial effects of lung cancer, whereby the size of a lesion can be measured in one dimension (diameter). Volumetric evaluation is desirable for estimating the size of a lesion accurately, but there are several constraints and limitations to calculating the volume in clinical trials. In this study, we developed a method to detect lesions automatically, with minimal intervention by the user, and calculate their volume. Our proposed method, called a spherical region-growing method (SPRG), uses segmentation that starts from a seed point set by the user. SPRG is a modification of an existing region-growing method that is based on a sphere instead of pixels. The SPRG method detects lesions while preventing leakage to neighboring tissues, because the sphere is grown, i.e., neighboring voxels are added, only when all the voxels meet the required conditions. In this study, two radiologists segmented lung tumors using a manual method and the proposed method, and the results of both methods were compared. The proposed method showed a high sensitivity of 81.68-84.81% and a high dice similarity coefficient (DSC) of 0.86-0.88 compared with the manual method. In addition, the SPRG intraclass correlation coefficient (ICC) was 0.998 (CI 0.997-0.999, p < 0.01), showing that the SPRG method is highly reliable. If our proposed method is used for segmentation and volumetric measurement of lesions, then objective and accurate results and shorter data analysis time are possible.
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Affiliation(s)
- Young Jae Kim
- Department of Biomedical Engineering, Gachon University College of Medicine, 21, Namdong-daero 774 beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea
- Department of Plazma Bio Display, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul, 01897, Republic of Korea
| | - Seung Hyun Lee
- Department of Plazma Bio Display, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul, 01897, Republic of Korea
| | - Kun Young Lim
- Department of Diagnostic Radiology, Center for Lung Cancer, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang, 10408, Gyeonggi-do, Republic of Korea
| | - Kwang Gi Kim
- Department of Biomedical Engineering, Gachon University College of Medicine, 21, Namdong-daero 774 beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea.
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Daniels CJ, Gallagher FA. Unsupervised Segmentation of 5D Hyperpolarized Carbon-13 MRI Data Using a Fuzzy Markov Random Field Model. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:840-850. [PMID: 28880161 DOI: 10.1109/tmi.2017.2737232] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Hyperpolarized MRI with 13C-labelled compounds is an emerging clinical technique allowing in vivo metabolic processes to be characterized non-invasively. Accurate quantification of 13C data, both for clinical and research purposes, typically relies on the use of region-of-interest analysis to detect and compare regions of altered metabolism. However, it is not clear how this should be determined from the five-dimensional data produced and most standard methodologies are unable to exploit the multidimensional nature of the data. Here we propose a solution to the novel problem of 13C image segmentation using a hybrid Markov random field model with continuous fuzzy logic. The algorithm fully utilizes the multi-dimensional data format in order to classify each voxel into one of six distinct classes based on its metabolic characteristics. Bayesian priors fully incorporate spatial, temporal and ratiometric contextual information whilst image contrast from multiple spectral dimensions are considered concurrently by using an analogy from color image segmentation. Performance of the algorithm is demonstrated on in silico data, where the superiority of the approach over a reference thresholding method is consistently observed. Application to in vivo animal data from a pre-clinical subcutaneous tumor model illustrates the ability of the MRF algorithm to successfully detect tumor location whilst avoiding image artifacts. This work has the potential to assist the analysis of human hyperpolarized 13C data in the future.
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Yepes-Calderon F, Hwang D, Johnson R, Bhushan D, Gajawelli N, Yong S, Quinn B, Yap F, Gill I, Lepore N, Duddalwar V. EdgeRunner: a novel shape-based pipeline for tumours analysis and characterisation. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2016. [DOI: 10.1080/21681163.2016.1177797] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Fernando Yepes-Calderon
- Department of Radiology, Keck School of Medicine – USC, Los Angeles, CA, USA
- Children Hospital Los Angeles, Los Angeles, CA, USA
| | - Darryl Hwang
- Department of Radiology, Keck School of Medicine – USC, Los Angeles, CA, USA
| | - Rebecca Johnson
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Desai Bhushan
- Department of Radiology, Keck School of Medicine – USC, Los Angeles, CA, USA
| | - Niharika Gajawelli
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Steven Yong
- Department of Radiology, Keck School of Medicine – USC, Los Angeles, CA, USA
| | - Brian Quinn
- Department of Radiology, Keck School of Medicine – USC, Los Angeles, CA, USA
| | - Felix Yap
- Department of Radiology, Keck School of Medicine – USC, Los Angeles, CA, USA
| | | | - Natasha Lepore
- Children Hospital Los Angeles, Los Angeles, CA, USA
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Vinay Duddalwar
- Department of Radiology, Keck School of Medicine – USC, Los Angeles, CA, USA
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Ju W, Xiang D, Zhang B, Wang L, Kopriva I, Chen X. Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on PET-CT Images. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2015; 24:5854-5867. [PMID: 26462198 DOI: 10.1109/tip.2015.2488902] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Accurate lung tumor delineation plays an important role in radiotherapy treatment planning. Since the lung tumor has poor boundary in positron emission tomography (PET) images and low contrast in computed tomography (CT) images, segmentation of tumor in the PET and CT images is a challenging task. In this paper, we effectively integrate the two modalities by making fully use of the superior contrast of PET images and superior spatial resolution of CT images. Random walk and graph cut method is integrated to solve the segmentation problem, in which random walk is utilized as an initialization tool to provide object seeds for graph cut segmentation on the PET and CT images. The co-segmentation problem is formulated as an energy minimization problem which is solved by max-flow/min-cut method. A graph, including two sub-graphs and a special link, is constructed, in which one sub-graph is for the PET and another is for CT, and the special link encodes a context term which penalizes the difference of the tumor segmentation on the two modalities. To fully utilize the characteristics of PET and CT images, a novel energy representation is devised. For the PET, a downhill cost and a 3D derivative cost are proposed. For the CT, a shape penalty cost is integrated into the energy function which helps to constrain the tumor region during the segmentation. We validate our algorithm on a data set which consists of 18 PET-CT images. The experimental results indicate that the proposed method is superior to the graph cut method solely using the PET or CT is more accurate compared with the random walk method, random walk co-segmentation method, and non-improved graph cut method.
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Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features. Int J Biomed Imaging 2015; 2015:230830. [PMID: 26451137 PMCID: PMC4587437 DOI: 10.1155/2015/230830] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Revised: 08/24/2015] [Accepted: 09/01/2015] [Indexed: 11/17/2022] Open
Abstract
Computer-aided diagnostic (CAD) systems provide fast and reliable diagnosis for medical images. In this paper, CAD system is proposed to analyze and automatically segment the lungs and classify each lung into normal or cancer. Using 70 different patients' lung CT dataset, Wiener filtering on the original CT images is applied firstly as a preprocessing step. Secondly, we combine histogram analysis with thresholding and morphological operations to segment the lung regions and extract each lung separately. Amplitude-Modulation Frequency-Modulation (AM-FM) method thirdly, has been used to extract features for ROIs. Then, the significant AM-FM features have been selected using Partial Least Squares Regression (PLSR) for classification step. Finally, K-nearest neighbour (KNN), support vector machine (SVM), naïve Bayes, and linear classifiers have been used with the selected AM-FM features. The performance of each classifier in terms of accuracy, sensitivity, and specificity is evaluated. The results indicate that our proposed CAD system succeeded to differentiate between normal and cancer lungs and achieved 95% accuracy in case of the linear classifier.
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Mu W, Chen Z, Shen W, Yang F, Liang Y, Dai R, Wu N, Tian J. A Segmentation Algorithm for Quantitative Analysis of Heterogeneous Tumors of the Cervix With ¹⁸F-FDG PET/CT. IEEE Trans Biomed Eng 2015; 62:2465-79. [PMID: 25993699 DOI: 10.1109/tbme.2015.2433397] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
As positron-emission tomography (PET) images have low spatial resolution and much noise, accurate image segmentation is one of the most challenging issues in tumor quantification. Tumors of the uterine cervix present a particular challenge because of urine activity in the adjacent bladder. Here, we propose and validate an automatic segmentation method adapted to cervical tumors. Our proposed methodology combined the gradient field information of both the filtered PET image and the level set function into a level set framework by constructing a new evolution equation. Furthermore, we also constructed a new hyperimage to recognize a rough tumor region using the fuzzy c-means algorithm according to the tissue specificity as defined by both PET (uptake) and computed tomography (attenuation) to provide the initial zero level set, which could make the segmentation process fully automatic. The proposed method was verified based on simulation and clinical studies. For simulation studies, seven different phantoms, representing tumors with homogenous/heterogeneous-low/high uptake patterns and different volumes, were simulated with five different noise levels. Twenty-seven cervical cancer patients at different stages were enrolled for clinical evaluation of the method. Dice similarity coefficients (DSC) and Hausdorff distance (HD) were used to evaluate the accuracy of the segmentation method, while a Bland-Altman analysis of the mean standardized uptake value (SUVmean) and metabolic tumor volume (MTV) was used to evaluate the accuracy of the quantification. Using this method, the DSCs and HDs of the homogenous and heterogeneous phantoms under clinical noise level were 93.39 ±1.09% and 6.02 ±1.09 mm, 93.59 ±1.63% and 8.92 ±2.57 mm, respectively. The DSCs and HDs in patients measured 91.80 ±2.46% and 7.79 ±2.18 mm. Through Bland-Altman analysis, the SUVmean and the MTV using our method showed high correlation with the clinical gold standard. The results of both simulation and clinical studies demonstrated the accuracy, effectiveness, and robustness of the proposed method. Further assessment of the quantitative indices indicates the feasibility of this algorithm in accurate quantitative analysis of cervical tumors in clinical practice.
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