1
|
De A, Das N, Saha PK, Comellas A, Hoffman E, Basu S, Chakraborti T. MSO-GP: 3-D segmentation of large and complex conjoined tree structures. Methods 2024; 229:9-16. [PMID: 38838947 DOI: 10.1016/j.ymeth.2024.05.016] [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: 10/22/2023] [Revised: 05/03/2024] [Accepted: 05/29/2024] [Indexed: 06/07/2024] Open
Abstract
Robust segmentation of large and complex conjoined tree structures in 3-D is a major challenge in computer vision. This is particularly true in computational biology, where we often encounter large data structures in size, but few in number, which poses a hard problem for learning algorithms. We show that merging multiscale opening with geodesic path propagation, can shed new light on this classic machine vision challenge, while circumventing the learning issue by developing an unsupervised visual geometry approach (digital topology/morphometry). The novelty of the proposed MSO-GP method comes from the geodesic path propagation being guided by a skeletonization of the conjoined structure that helps to achieve robust segmentation results in a particularly challenging task in this area, that of artery-vein separation from non-contrast pulmonary computed tomography angiograms. This is an important first step in measuring vascular geometry to then diagnose pulmonary diseases and to develop image-based phenotypes. We first present proof-of-concept results on synthetic data, and then verify the performance on pig lung and human lung data with less segmentation time and user intervention needs than those of the competing methods.
Collapse
Affiliation(s)
- Arijit De
- Department of Electronics & Telecommunication Engineering, Jadavpur University, Kolkata, India.
| | - Nirmal Das
- Department of Computer Science and Engineering (AIML), Institute of Engineering and Management, Kolkata, India; Department of Computer Science and Engineering, Jadavpur University, Kolkata, India.
| | - Punam K Saha
- Department of Electrical and Computer Engineering & Department of Radiology, University of Iowa, Iowa City, IA 52242, USA.
| | | | - Eric Hoffman
- Department of Internal Medicine, University of Iowa, Iowa City, USA.
| | - Subhadip Basu
- Department of Computer Science and Engineering (AIML), Institute of Engineering and Management, Kolkata, India.
| | - Tapabrata Chakraborti
- Health Sciences Programme, The Alan Turing Institute, London, UK; Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
| |
Collapse
|
2
|
Pu J, Bandos A, Yu T, Wang R, Yuan JM, Herman J, Wilson D. Pulmonary circulatory system characteristics are associated with future lung cancer risk. Med Phys 2024; 51:2589-2597. [PMID: 38159298 PMCID: PMC10994761 DOI: 10.1002/mp.16930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 12/14/2023] [Accepted: 12/20/2023] [Indexed: 01/03/2024] Open
Abstract
BACKGROUND Most of the subjects eligible for annual low-dose computed tomography (LDCT) lung screening will not develop lung cancer for their life. It is important to identify novel biomarkers that can help identify those at risk of developing lung cancer and improve the efficiency of LDCT screening programs. OBJECTIVE This study aims to investigate the association between the morphology of the pulmonary circulatory system (PCS) and lung cancer development using LDCT scans acquired in the screening setting. METHODS We analyzed the PLuSS cohort of 3635 lung screening patients from 2002 to 2016. Circulatory structures were segmented and quantified from LDCT scans. The time from the baseline CT scan to lung cancer diagnosis, accounting for death, was used to evaluate the prognostic ability (i.e., hazard ratio (HR)) of these structures independently and with demographic factors. Five-fold cross-validation was used to evaluate prognostic scores. RESULTS Intrapulmonary vein volume had the strongest association with future lung cancer (HR = 0.63, p < 0.001). The joint model of intrapulmonary vein volume, age, smoking status, and clinical emphysema provided the strongest prognostic ability (HR = 2.20, AUC = 0.74). The addition of circulatory structures improved risk stratification, identifying the top 10% with 28% risk of lung cancer within 15 years. CONCLUSION PCS characteristics, particularly intrapulmonary vein volume, are important predictors of lung cancer development. These factors significantly improve prognostication based on demographic factors and noncirculatory patient characteristics, particularly in the long term. Approximately 10% of the population can be identified with risk several times greater than average.
Collapse
Affiliation(s)
- Jiantao Pu
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Cancer Epidemiology and Prevention Program, UPMC Hillman Cancer Center
| | - Andriy Bandos
- Department of Biostatistics, University of Pittsburgh, PA 15213, USA
| | - Tong Yu
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Renwei Wang
- Cancer Epidemiology and Prevention Program, UPMC Hillman Cancer Center
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Jian-min Yuan
- Cancer Epidemiology and Prevention Program, UPMC Hillman Cancer Center
- Division of Hematology/Oncology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - James Herman
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - David Wilson
- Cancer Epidemiology and Prevention Program, UPMC Hillman Cancer Center
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| |
Collapse
|
3
|
Suzuki H, Kawata Y, Aokage K, Matsumoto Y, Sugiura T, Tanabe N, Nakano Y, Tsuchida T, Kusumoto M, Marumo K, Kaneko M, Niki N. Aorta and main pulmonary artery segmentation using stacked U-Net and localization on non-contrast-enhanced computed tomography images. Med Phys 2024; 51:1232-1243. [PMID: 37519027 DOI: 10.1002/mp.16654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 07/17/2023] [Accepted: 07/17/2023] [Indexed: 08/01/2023] Open
Abstract
BACKGROUND The contact between the aorta, main pulmonary artery (MPA), main pulmonary vein, vena cava (VC), and esophagus affects segmentation of the aorta and MPA in non-contrast-enhanced computed tomography (NCE-CT) images. PURPOSE A two-stage stacked U-Net and localization of the aorta and MPA were developed for the segmentation of the aorta and MPA in NCE-CT images. METHODS Normal-dose NCE-CT images of 24 subjects with chronic thromboembolic pulmonary hypertension (CTEPH) and low-dose NCE-CT images of 100 subjects without CTEPH were used in this study. The aorta is in contact with the ascending aorta (AA) and MPA, the AA with the VC, the aortic arch (AR) with the VC and esophagus, and the descending aorta (DA) with the esophagus. These contact surfaces were manually annotated. The contact surfaces were quantified using the contact surface ratio (CSR). Segmentation of the aorta and MPA in NCE-CT images was performed by localization of the aorta and MPA and a two-stage stacked U-Net. Localization was performed by extracting and processing the trachea and main bronchus. The first stage of the stacked U-Net consisted of a 2D U-Net, 2D U-Net with a pre-trained VGG-16 encoder, and 2D attention U-Net. The second stage consisted of a 3D U-Net with four input channels: the CT volume and three segmentation results of the first stage. The model was trained and tested using 10-fold cross-validation. Segmentation of the entire volume was evaluated using the Dice similarity coefficient (DSC). Segmentation of the contact area was also assessed using the mean surface distance (MSD). The statistical analysis of the evaluation underwent a multi-comparison correction. CTEPH and non-CTEPH cases were classified based on the vessel diameters measured from the segmented MPA. RESULTS For the noncontact surfaces of AA, the MSD of stacked U-Net was 0.31 ± 0.10 mm (p < 0.05) and 0.32 ± 0.13 mm (p < 0.05) for non-CTEPH and CTEPH cases, respectively. For contact surfaces with a CSR of 0.4 or greater in AA, the MSD was 0.52 ± 0.23 mm (p < 0.05), and 0.68 ± 0.29 mm (p > 0.05) for non-CTEPH and CTEPH cases, respectively. MSDs were lower than those of 2D and 3D U-Nets for contact and noncontact surfaces; moreover, MSDs increased slightly with larger CSRs. However, the stacked U-Net achieved MSDs of approximately 1 pixel for a wide contact surface. The area under the receiver operating characteristic curve for CTEPH and non-CTEPH classification using the right main pulmonary artery (RMPA) diameter was 0.97 (95% confidence interval [CI]: 0.94-1.00). CONCLUSIONS Segmentation of the aorta and MPA on NCE-CT images were affected by vascular and esophageal contact. The application of stacked U-Net and localization techniques for non-CTEPH and CTEPH cases mitigated the impact of contact, suggesting its potential for diagnosing CTEPH.
Collapse
Affiliation(s)
- Hidenobu Suzuki
- Faculty of Science and Technology, Tokushima University, Tokushima, Japan
| | - Yoshiki Kawata
- Institute of Post-LED Photonics, Tokushima University, Tokushima, Japan
| | - Keiju Aokage
- Department of Thoracic Surgery, National Cancer Center Hospital East, Chiba, Japan
| | - Yuji Matsumoto
- Department of Endoscopy, Respiratory Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Toshihiko Sugiura
- Department of Respirology, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Nobuhiro Tanabe
- Department of Respirology, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Yasutaka Nakano
- Division of Respiratory Medicine, Department of Internal Medicine, Shiga University of Medical Science, Shiga, Japan
| | - Takaaki Tsuchida
- Department of Endoscopy, Respiratory Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Masahiko Kusumoto
- Division of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, Japan
| | | | | | - Noboru Niki
- Faculty of Science and Technology, Tokushima University, Tokushima, Japan
| |
Collapse
|
4
|
Li R, Song M, Wang R, Su N, E L. Can CT-Based Arterial and Venous Morphological Markers of Chronic Obstructive Pulmonary Disease Explain Pulmonary Vascular Remodeling? Acad Radiol 2024; 31:22-34. [PMID: 37248100 DOI: 10.1016/j.acra.2023.04.026] [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: 02/26/2023] [Revised: 04/21/2023] [Accepted: 04/21/2023] [Indexed: 05/31/2023]
Abstract
RATIONALE AND OBJECTIVES We analyzed changes in quantitative pulmonary artery and vein parameters to investigate pulmonary vascular remodeling characteristics in chronic obstructive pulmonary disease (COPD) patients. MATERIALS AND METHODS This retrospective study recruited healthy volunteers and COPD patients. Participants undergoing standard-of-care pulmonary function testing (PFT) and computed tomography (CT) evaluations were classified into five groups: normal and Global Initiative for Chronic Obstructive Lung Disease (GOLD) grades 1-4. Artery and vein analyses (volumes, numbers, densities, and fractions) were performed using artificial intelligence. RESULTS Among 139 subjects (136 men; mean age, 64years±8 [SD]) with GOLD grade 1 (n = 13), grade 2 (n = 49), grade 3 (n = 42), grade 4 (n = 17) and control subjects (n = 18) enrolled, differences in arterial volumes (BV5-10, BV10+, pulmonary arterial volume) and venous densities (BV5 density, BV10+ density, pulmonary venous density, pulmonary venous branch density) among control and GOLD grades 1-4 were statistically significant (P < .05). Higher pulmonary arterial volumes and lower number were observed with more advanced COPD. The number and volumes of pulmonary veins were lower in GOLD grades 2 and 3 than in GOLD grade 1 but higher in GOLD grade 4 than in GOLD grade 3. The numbers and volumes of pulmonary arteries and veins showed varying positive correlations (γ = 0.18-0.96, P < .05). Pulmonary vascular densities were mildly to moderately correlated with PFT results (γ = 0.236-0.495, P < .05) and were moderately negatively correlated with the emphysema percentage (γ = -0.591 to -0.315, P < .05). CONCLUSION Patients with COPD exhibited pulmonary vascular remodeling, which occurred in the arteries at the early grade of COPD and in the veins at the late grade. CT-based quantitative analysis of pulmonary vasculature may become an imaging marker for early diagnosis and assessment of COPD severity.
Collapse
Affiliation(s)
- Rui Li
- Department of Radiology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China (R.L., M.S., R.W., N.S.); Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (R.L., M.S., R.W., N.S.)
| | - Mengyi Song
- Department of Radiology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China (R.L., M.S., R.W., N.S.); Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (R.L., M.S., R.W., N.S.)
| | - Ronghua Wang
- Department of Radiology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China (R.L., M.S., R.W., N.S.); Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (R.L., M.S., R.W., N.S.)
| | - Ningling Su
- Department of Radiology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China (R.L., M.S., R.W., N.S.); Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (R.L., M.S., R.W., N.S.)
| | - Linning E
- Department of Radiology, People's Hospital of Longhua, No. 38 Jinglong Construction Rd, Shenzhen 518109, China (L.E).
| |
Collapse
|
5
|
Zhou Q, Tan W, Li Q, Li B, Zhou L, Liu X, Yang J, Zhao D. A new segment method for pulmonary artery and vein. Health Inf Sci Syst 2023; 11:47. [PMID: 37810417 PMCID: PMC10558422 DOI: 10.1007/s13755-023-00245-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 09/01/2023] [Indexed: 10/10/2023] Open
Abstract
Accurate differentiation between pulmonary arteries and veins (A/V) holds pivotal importance in the realm of diagnosing and treating pulmonary ailments. This study presents a new approach that leverages grayscale differences between A/V. Distinctions are measured using median and mean grayscale values within the vessel area. Initially, adherent regions are removed based on vessel structure. The trunk regions are segmented using gray level information near the heart region of the lung boundary. Incorrectly segmented vessels are corrected based on connectivity. For distal lung vessels, a similar distance field is established using a graph-cut method. Experimental results show the algorithm's superior segmentation accuracy, achieving 97.26% compared to the CNN-based average accuracy of 91.67%. Error branches are more concentrated, aiding subsequent manual and automatic correction. This demonstrates the algorithm's effective segmentation of pulmonary A/V.
Collapse
Affiliation(s)
- Qinghua Zhou
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189 China
- College of Computer Science and Engineering, Northeastern University, Shenyang, 110189 China
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189 China
- College of Computer Science and Engineering, Northeastern University, Shenyang, 110189 China
| | - Qingya Li
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189 China
- College of Computer Science and Engineering, Northeastern University, Shenyang, 110189 China
| | - Baoting Li
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189 China
- College of Computer Science and Engineering, Northeastern University, Shenyang, 110189 China
| | - Luyu Zhou
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189 China
- College of Computer Science and Engineering, Northeastern University, Shenyang, 110189 China
| | - Xin Liu
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189 China
- College of Computer Science and Engineering, Northeastern University, Shenyang, 110189 China
| | - Jinzhu Yang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189 China
- College of Computer Science and Engineering, Northeastern University, Shenyang, 110189 China
| | - Dazhe Zhao
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189 China
- College of Computer Science and Engineering, Northeastern University, Shenyang, 110189 China
| |
Collapse
|
6
|
Pu J, Gezer NS, Ren S, Alpaydin AO, Avci ER, Risbano MG, Rivera-Lebron B, Chan SYW, Leader JK. Automated detection and segmentation of pulmonary embolisms on computed tomography pulmonary angiography (CTPA) using deep learning but without manual outlining. Med Image Anal 2023; 89:102882. [PMID: 37482032 PMCID: PMC10528048 DOI: 10.1016/j.media.2023.102882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 05/26/2023] [Accepted: 06/26/2023] [Indexed: 07/25/2023]
Abstract
We present a novel computer algorithm to automatically detect and segment pulmonary embolisms (PEs) on computed tomography pulmonary angiography (CTPA). This algorithm is based on deep learning but does not require manual outlines of the PE regions. Given a CTPA scan, both intra- and extra-pulmonary arteries were firstly segmented. The arteries were then partitioned into several parts based on size (radius). Adaptive thresholding and constrained morphological operations were used to identify suspicious PE regions within each part. The confidence of a suspicious region to be PE was scored based on its contrast in the arteries. This approach was applied to the publicly available RSNA Pulmonary Embolism CT Dataset (RSNA-PE) to identify three-dimensional (3-D) PE negative and positive image patches, which were used to train a 3-D Recurrent Residual U-Net (R2-Unet) to automatically segment PE. The feasibility of this computer algorithm was validated on an independent test set consisting of 91 CTPA scans acquired from a different medical institute, where the PE regions were manually located and outlined by a thoracic radiologist (>18 years' experience). An R2-Unet model was also trained and validated on the manual outlines using a 5-fold cross-validation method. The CNN model trained on the high-confident PE regions showed a Dice coefficient of 0.676±0.168 and a false positive rate of 1.86 per CT scan, while the CNN model trained on the manual outlines demonstrated a Dice coefficient of 0.647±0.192 and a false positive rate of 4.20 per CT scan. The former model performed significantly better than the latter model (p<0.01). The promising performance of the developed PE detection and segmentation algorithm suggests the feasibility of training a deep learning network without dedicating significant efforts to manual annotations of the PE regions on CTPA scans.
Collapse
Affiliation(s)
- Jiantao Pu
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA.
| | | | - Shangsi Ren
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | | | - Emre Ruhat Avci
- Department of Radiology, Dokuz Eylul University, Izmir, Turkey
| | - Michael G Risbano
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | | | | | - Joseph K Leader
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| |
Collapse
|
7
|
Zhang H, Zhang M, Gu Y, Yang GZ. Deep anatomy learning for lung airway and artery-vein modeling with contrast-enhanced CT synthesis. Int J Comput Assist Radiol Surg 2023:10.1007/s11548-023-02946-7. [PMID: 37259009 DOI: 10.1007/s11548-023-02946-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 05/02/2023] [Indexed: 06/02/2023]
Abstract
PURPOSE Endobronchial intervention requires detailed modeling of pulmonary anatomical substructure, such as lung airway and artery-vein maps, which are commonly extracted from non-contrast computed tomography (NCCT) independently using automatic segmentation approaches. We aim to make the first attempt to jointly train a CNN-based model for airway and artery-vein segmentation along with synthetic contrast-enhanced CT (CECT) generation. METHODS A multi-task framework is proposed to simultaneously generate three segmentation maps and synthesize CECTs. We first design a collaborative learning model with tissue knowledge interaction for lung airway and artery-vein segmentation. Meanwhile, a conditional adversarial training strategy is applied to generate CECTs from NCCTs guided by artery maps. Additionally, CECT identity and reconstruction help to regularize the model for plausible NCCT to CECT translation. RESULTS Extensive experiments were conducted to evaluate the performance of the proposed framework based on three datasets (90 NCCTs for the airway task, 55 NCCTs for the artery-vein task and 100 CECTs for the artery task). The results demonstrate the effective improvement of our proposed method compared to other methods and configurations that can achieve more accurate segmentation maps (Dice score coefficients for these three tasks are: 93.6%, 80.7% and 82.4%, respectively) and realistic CECTs simultaneously. The ablation study further verifies the effectiveness of the components of the designed model. CONCLUSION This study demonstrates the model potential in multi-task learning that integrates anatomically relevant segmentation and performs NCCT to CECT translation. Such an interaction approach promotes mutually for both promising segmentation results and plausible synthesis.
Collapse
Affiliation(s)
- Hanxiao Zhang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Minghui Zhang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Yun Gu
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China.
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, China.
| | - Guang-Zhong Yang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China.
| |
Collapse
|
8
|
Rhyou SY, Yoo JC. Aggregated micropatch-based deep learning neural network for ultrasonic diagnosis of cirrhosis. Artif Intell Med 2023; 139:102541. [PMID: 37100510 DOI: 10.1016/j.artmed.2023.102541] [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: 10/14/2022] [Revised: 03/15/2023] [Accepted: 03/27/2023] [Indexed: 04/28/2023]
Abstract
Despite the advancements in the diagnosis of early-stage cirrhosis, the accuracy in the diagnosis using ultrasound is still challenging owing to the presence of various image artifacts, which results in poor visual quality of the textural and lower-frequency components. In this study, we propose an end-to-end multistep network called CirrhosisNet that includes two transfer-learned convolutional neural networks for semantic segmentation and classification tasks. It uses a uniquely designed image, called an aggregated micropatch (AMP), as an input image to the classification network, thereby assessing whether the liver is in a cirrhotic stage. With a prototype AMP image, we synthesized a bunch of AMP images while retaining the textural features. This synthesis significantly increases the number of insufficient cirrhosis-labeled images, thereby circumventing overfitting issues and optimizing network performance. Furthermore, the synthesized AMP images contained unique textural patterns, mostly generated on the boundaries between adjacent micropatches (μ-patches) during their aggregation. These newly created boundary patterns provide rich information regarding the texture features of the ultrasound image, thereby making cirrhosis diagnosis more accurate and sensitive. The experimental results demonstrated that our proposed AMP image synthesis is extremely effective in expanding the dataset of cirrhosis images, thus diagnosing liver cirrhosis with considerably high accuracy. We achieved an accuracy of 99.95 %, a sensitivity of 100 %, and a specificity of 99.9 % on the Samsung Medical Center dataset using 8 × 8 pixels-sized μ-patches. The proposed approach provides an effective solution to deep-learning models with limited-training data, such as medical imaging tasks.
Collapse
Affiliation(s)
- Se-Yeol Rhyou
- Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 440-746, South Korea
| | - Jae-Chern Yoo
- Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 440-746, South Korea.
| |
Collapse
|
9
|
Gezer NS, Bandos AI, Beeche CA, Leader JK, Dhupar R, Pu J. CT-derived body composition associated with lung cancer recurrence after surgery. Lung Cancer 2023; 179:107189. [PMID: 37058786 PMCID: PMC10166196 DOI: 10.1016/j.lungcan.2023.107189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 03/24/2023] [Accepted: 04/07/2023] [Indexed: 04/16/2023]
Abstract
OBJECTIVES To evaluate the impact of body composition derived from computed tomography (CT) scans on postoperative lung cancer recurrence. METHODS We created a retrospective cohort of 363 lung cancer patients who underwent lung resections and had verified recurrence, death, or at least 5-year follow-up without either event. Five key body tissues and ten tumor features were automatically segmented and quantified based on preoperative whole-body CT scans (acquired as part of a PET-CT scan) and chest CT scans, respectively. Time-to-event analysis accounting for the competing event of death was performed to analyze the impact of body composition, tumor features, clinical information, and pathological features on lung cancer recurrence after surgery. The hazard ratio (HR) of normalized factors was used to assess individual significance univariately and in the combined models. The 5-fold cross-validated time-dependent receiver operating characteristics analysis, with an emphasis on the area under the 3-year ROC curve (AUC), was used to characterize the ability to predict lung cancer recurrence. RESULTS Body tissues that showed a standalone potential to predict lung cancer recurrence include visceral adipose tissue (VAT) volume (HR = 0.88, p = 0.047), subcutaneous adipose tissue (SAT) density (HR = 1.14, p = 0.034), inter-muscle adipose tissue (IMAT) volume (HR = 0.83, p = 0.002), muscle density (HR = 1.27, p < 0.001), and total fat volume (HR = 0.89, p = 0.050). The CT-derived muscular and tumor features significantly contributed to a model including clinicopathological factors, resulting in an AUC of 0.78 (95% CI: 0.75-0.83) to predict recurrence at 3 years. CONCLUSIONS Body composition features (e.g., muscle density, or muscle and inter-muscle adipose tissue volumes) can improve the prediction of recurrence when combined with clinicopathological factors.
Collapse
Affiliation(s)
- Naciye S Gezer
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Andriy I Bandos
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Cameron A Beeche
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Joseph K Leader
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Rajeev Dhupar
- Department of Cardiothoracic Surgery, Division of Thoracic and Foregut Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA; Surgical Services Division, Thoracic Surgery, VA Pittsburgh Healthcare System, Pittsburgh, PA 15213, USA.
| | - Jiantao Pu
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA.
| |
Collapse
|
10
|
Iyer K, Beeche CA, Gezer NS, Leader JK, Ren S, Dhupar R, Pu J. CT-Derived Body Composition Is a Predictor of Survival after Esophagectomy. J Clin Med 2023; 12:2106. [PMID: 36983109 PMCID: PMC10058526 DOI: 10.3390/jcm12062106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/27/2023] [Accepted: 03/04/2023] [Indexed: 03/11/2023] Open
Abstract
BACKGROUND Body composition can be accurately quantified based on computed tomography (CT) and typically reflects an individual's overall health status. However, there is a dearth of research examining the relationship between body composition and survival following esophagectomy. METHODS We created a cohort consisting of 183 patients who underwent esophagectomy for esophageal cancer without neoadjuvant therapy. The cohort included preoperative PET-CT scans, along with pathologic and clinical data, which were collected prospectively. Radiomic, tumor, PET, and body composition features were automatically extracted from the images. Cox regression models were utilized to identify variables associated with survival. Logistic regression and machine learning models were developed to predict one-, three-, and five-year survival rates. Model performance was evaluated based on the area under the receiver operating characteristics curve (ROC/AUC). To test for the statistical significance of the impact of body composition on survival, body composition features were excluded for the best-performing models, and the DeLong test was used. RESULTS The one-year survival model contained 10 variables, including three body composition variables (bone mass, bone density, and visceral adipose tissue (VAT) density), and demonstrated an AUC of 0.817 (95% CI: 0.738-0.897). The three-year survival model incorporated 14 variables, including three body composition variables (intermuscular adipose tissue (IMAT) volume, IMAT mass, and bone mass), with an AUC of 0.693 (95% CI: 0.594-0.792). For the five-year survival model, 10 variables were included, of which two were body composition variables (intramuscular adipose tissue (IMAT) volume and visceral adipose tissue (VAT) mass), with an AUC of 0.861 (95% CI: 0.783-0.938). The one- and five-year survival models exhibited significantly inferior performance when body composition features were not incorporated. CONCLUSIONS Body composition features derived from preoperative CT scans should be considered when predicting survival following esophagectomy.
Collapse
Affiliation(s)
- Kartik Iyer
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Cameron A. Beeche
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Naciye S. Gezer
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Joseph K. Leader
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Shangsi Ren
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Rajeev Dhupar
- Department of Cardiothoracic Surgery, Division of Thoracic and Foregut Surgery, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Surgical Services Division, Thoracic Surgery, VA Pittsburgh Healthcare System, Pittsburgh, PA 15213, USA
| | - Jiantao Pu
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
| |
Collapse
|
11
|
Wallat EM, Wuschner AE, Flakus MJ, Gerard SE, Christensen GE, Reinhardt JM, Bayouth JE. Predicting pulmonary ventilation damage after radiation therapy for nonsmall cell lung cancer using a ResNet generative adversarial network. Med Phys 2023; 50:3199-3209. [PMID: 36779695 DOI: 10.1002/mp.16311] [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: 08/27/2022] [Revised: 02/01/2023] [Accepted: 02/06/2023] [Indexed: 02/14/2023] Open
Abstract
BACKGROUND Functional lung avoidance radiation therapy (RT) is a technique being investigated to preferentially avoid specific regions of the lung that are predicted to be more susceptible to radiation-induced damage. Reducing the dose delivered to high functioning regions may reduce the occurrence radiation-induced lung injuries (RILIs) and toxicities. However, in order to develop effective lung function-sparing plans, accurate predictions of post-RT ventilation change are needed to determine which regions of the lung should be spared. PURPOSE To predict pulmonary ventilation change following RT for nonsmall cell lung cancer using machine learning. METHODS A conditional generative adversarial network (cGAN) was developed with data from 82 human subjects enrolled in a randomized clinical trial approved by the institution's IRB to predict post-RT pulmonary ventilation change. The inputs to the network were the pre-RT pulmonary ventilation map and radiation dose distribution. The loss function was a combination of the binary cross-entropy loss and an asymmetrical structural similarity index measure (aSSIM) function designed to increase penalization of under-prediction of ventilation damage. Network performance was evaluated against a previously developed polynomial regression model using a paired sample t-test for comparison. Evaluation was performed using eight-fold cross-validation. RESULTS From the eight-fold cross-validation, we found that relative to the polynomial model, the cGAN model significantly improved predicting regions of ventilation damage following radiotherapy based on true positive rate (TPR), 0.14±0.15 to 0.72±0.21, and Dice similarity coefficient (DSC), 0.19±0.16 to 0.46±0.14, but significantly declined in true negative rate, 0.97±0.05 to 0.62±0.21, and accuracy, 0.79±0.08 to 0.65±0.14. Additionally, the average true positive volume increased from 104±119 cc in the POLY model to 565±332 cc in the cGAN model, and the average false negative volume decreased from 654±361 cc in the POLY model to 193±163 cc in the cGAN model. CONCLUSIONS The proposed cGAN model demonstrated significant improvement in TPR and DSC. The higher sensitivity of the cGAN model can improve the clinical utility of functional lung avoidance RT by identifying larger volumes of functional lung that can be spared and thus decrease the probability of the patient developing RILIs.
Collapse
Affiliation(s)
- Eric M Wallat
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Antonia E Wuschner
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Mattison J Flakus
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Sarah E Gerard
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA
| | - Gary E Christensen
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA.,Department of Radiation Oncology, University of Iowa, Iowa City, Iowa, USA
| | - Joseph M Reinhardt
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA.,Department of Radiology, University of Iowa, Iowa City, Iowa, USA
| | - John E Bayouth
- Department of Radiation Medicine, Oregon Health & Science University, Portland, Oregon, USA
| |
Collapse
|