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Qi HF, Sun XQ, Du HK, Li JH, Zhang LY, Xi YG. Features of MR signals of retroplacental basal decidual space and its diagnostic significance. Technol Health Care 2024; 32:727-734. [PMID: 37545268 DOI: 10.3233/thc-230098] [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] [Indexed: 08/08/2023]
Abstract
BACKGROUND With more pregnant women undergoing cesarean section, the number of women with scarring in the uterus undergoing uterine magnetic resonance (MR) examination in the second and third trimesters following a subsequent pregnancy, has increased. OBJECTIVE To investigate features of MR signals in retroplacental basal decidual space. METHODS The MR imaging data of patients with clinically and pathologically confirmed placenta implantation and complete placental abruption were retrospectively analyzed. RESULTS Patients with high-intensity signals in T2-weighted images (T2WI) of the retroplacental basal decidual space did not suffer placenta implantation after delivery, while high-intensity signals in T2WI of the retroplacental basal decidual space was not observed in patients with different degrees of placenta implantation. CONCLUSION As the retroplacental basal decidual space is the barrier between the placenta and myometrium, high-intensity signals in T2WI can improve the confidence of MR exclusion diagnostics of placenta implantation, and can be used as exclusion criteria for MR diagnosis of placenta implantation.
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Herrera CL, Wang Y, Udayakumar D, Xi Y, Do QN, Lewis MA, Owen DM, Fei B, Spong CY, Twickler DM, Madhuranthakam AJ. Longitudinal assessment of placental perfusion in normal and hypertensive pregnancies using pseudo-continuous arterial spin-labeled MRI: preliminary experience. Eur Radiol 2023; 33:9223-9232. [PMID: 37466705 PMCID: PMC10796849 DOI: 10.1007/s00330-023-09945-x] [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: 08/07/2022] [Revised: 05/05/2023] [Accepted: 05/17/2023] [Indexed: 07/20/2023]
Abstract
OBJECTIVES To evaluate longitudinal placental perfusion using pseudo-continuous arterial spin-labeled (pCASL) MRI in normal pregnancies and in pregnancies affected by chronic hypertension (cHTN), who are at the greatest risk for placental-mediated disease conditions. METHODS Eighteen normal and 23 pregnant subjects with cHTN requiring antihypertensive therapy were scanned at 3 T using free-breathing pCASL-MRI at 16-20 and 24-28 weeks of gestational age. RESULTS Mean placental perfusion was 103.1 ± 48.0 and 71.4 ± 18.3 mL/100 g/min at 16-20 and 24-28 weeks respectively in normal pregnancies and 79.4 ± 27.4 and 74.9 ± 26.6 mL/100 g/min in cHTN pregnancies. There was a significant decrease in perfusion between the first and second scans in normal pregnancies (p = 0.004), which was not observed in cHTN pregnancies (p = 0.36). The mean perfusion was not statistically different between normal and cHTN pregnancies at both scans, but the absolute change in perfusion per week was statistically different between these groups (p = 0.044). Furthermore, placental perfusion was significantly lower at both time points (p = 0.027 and 0.044 respectively) in the four pregnant subjects with cHTN who went on to have infants that were small for gestational age (52.7 ± 20.4 and 50.4 ± 20.9 mL/100 g/min) versus those who did not (85 ± 25.6 and 80.0 ± 25.1 mL/100 g/min). CONCLUSION pCASL-MRI enables longitudinal assessment of placental perfusion in pregnant subjects. Placental perfusion in the second trimester declined in normal pregnancies whereas it remained unchanged in cHTN pregnancies, consistent with alterations due to vascular disease pathology. Perfusion was significantly lower in those with small for gestational age infants, indicating that pCASL-MRI-measured perfusion may be an effective imaging biomarker for placental insufficiency. CLINICAL RELEVANCE STATEMENT pCASL-MRI enables longitudinal assessment of placental perfusion without administering exogenous contrast agent and can identify placental insufficiency in pregnant subjects with chronic hypertension that can lead to earlier interventions. KEY POINTS • Arterial spin-labeled (ASL) magnetic resonance imaging (MRI) enables longitudinal assessment of placental perfusion without administering exogenous contrast agent. • ASL-MRI-measured placental perfusion decreased significantly between 16-20 week and 24-28 week gestational age in normal pregnancies, while it remained relatively constant in hypertensive pregnancies, attributed to vascular disease pathology. • ASL-MRI-measured placental perfusion was significantly lower in subjects with hypertension who had a small for gestational age infant at 16-20-week gestation, indicating perfusion as an effective biomarker of placental insufficiency.
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Affiliation(s)
- Christina L Herrera
- Department of Obstetrics and Gynecology, UT Southwestern Medical Center, Dallas, TX, USA
- Parkland Health and Hospital System, Dallas, TX, USA
| | - Yiming Wang
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390-9061, USA
| | - Durga Udayakumar
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390-9061, USA
- Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, USA
| | - Yin Xi
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390-9061, USA
- Department of Clinical Science, UT Southwestern Medical Center, Dallas, TX, USA
| | - Quyen N Do
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390-9061, USA
| | - Matthew A Lewis
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390-9061, USA
| | - David M Owen
- Department of Obstetrics and Gynecology, UT Southwestern Medical Center, Dallas, TX, USA
- Parkland Health and Hospital System, Dallas, TX, USA
| | - Baowei Fei
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390-9061, USA
- Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, USA
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA
| | - Catherine Y Spong
- Department of Obstetrics and Gynecology, UT Southwestern Medical Center, Dallas, TX, USA
- Parkland Health and Hospital System, Dallas, TX, USA
| | - Diane M Twickler
- Department of Obstetrics and Gynecology, UT Southwestern Medical Center, Dallas, TX, USA
- Parkland Health and Hospital System, Dallas, TX, USA
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390-9061, USA
| | - Ananth J Madhuranthakam
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390-9061, USA.
- Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, USA.
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Wu X, Yang H, Yu X, Zeng J, Qiao J, Qi H, Xu H. The prenatal diagnostic indicators of placenta accreta spectrum disorders. Heliyon 2023; 9:e16241. [PMID: 37234657 PMCID: PMC10208845 DOI: 10.1016/j.heliyon.2023.e16241] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 04/29/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
Placenta accreta spectrum (PAS) disorders refers to a heterogeneous group of anomalies distinguished by abnormal adhesion or invasion of chorionic villi through the myometrium and uterine serosa. PAS frequently results in life-threatening complications, including postpartum hemorrhage and hysterotomy. The incidence of PAS has increased recently as a result of rising cesarean section rates. Consequently, prenatal screening for PAS is essential. Despite the need to increase specificity, ultrasound is still considered a primary adjunct. Given the dangers and adverse effects of PAS, it is necessary to identify pertinent markers and validate indicators to improve prenatal diagnosis. This article summarizes the predictors regarding biomarkers, ultrasound indicators, and magnetic resonance imaging (MRI) features. In addition, we discuss the effectiveness of joint diagnosis and the most recent research on PAS. In particular, we focus on (a) posterior placental implantation and (b) accreta after in vitro fertilization-embryo transfer, both of which have low diagnostic rates. At last, we graphically display the prenatal diagnostic indicators and each diagnostic performance.
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Affiliation(s)
- Xiafei Wu
- Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Huan Yang
- Department of Obstetrics, Chongqing University Three Gorges Hospital, Chongqing 404100, China
| | - Xinyang Yu
- Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Jing Zeng
- Stomatological Hospital of Chongqing Medical University, Chongqing 401147, China
| | - Juan Qiao
- Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Hongbo Qi
- Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
- Women and Children's Hospital of Chongqing Medical University, Chongqing 401147, China
| | - Hongbing Xu
- Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
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Automatic placental and fetal volume estimation by a convolutional neural network. Placenta 2023; 134:23-29. [PMID: 36863128 DOI: 10.1016/j.placenta.2023.02.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 02/21/2023] [Accepted: 02/24/2023] [Indexed: 03/03/2023]
Abstract
INTRODUCTION We aimed to develop an artificial intelligence (AI) deep learning algorithm to efficiently estimate placental and fetal volumes from magnetic resonance (MR) scans. METHODS Manually annotated images from an MRI sequence was used as input to the neural network DenseVNet. We included data from 193 normal pregnancies at gestational week 27 and 37. The data were split into 163 scans for training, 10 scans for validation and 20 scans for testing. The neural network segmentations were compared to the manual annotation (ground truth) using the Dice Score Coefficient (DSC). RESULTS The mean ground truth placental volume at gestational week 27 and 37 was 571 cm3 (Standard Deviation (SD) 293 cm3) and 853 cm3 (SD 186 cm3), respectively. Mean fetal volume was 979 cm3 (SD 117 cm3) and 2715 cm3 (SD 360 cm3). The best fitting neural network model was attained at 22,000 training iterations with mean DSC 0.925 (SD 0.041). The neural network estimated mean placental volumes at gestational week 27-870 cm3 (SD 202 cm3) (DSC 0.887 (SD 0.034), and to 950 cm3 (SD 316 cm3) at gestational week 37 (DSC 0.896 (SD 0.030)). Mean fetal volumes were 1292 cm3 (SD 191 cm3) and 2712 cm3 (SD 540 cm3), with mean DSC of 0.952 (SD 0.008) and 0.970 (SD 0.040). The time spent for volume estimation was reduced from 60 to 90 min by manual annotation, to less than 10 s by the neural network. CONCLUSION The correctness of neural network volume estimation is comparable to human performance; the efficiency is substantially improved.
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Huang J, Do QN, Shahed M, Xi Y, Lewis MA, Herrera CL, Owen D, Spong CY, Madhuranthakam AJ, Twickler DM, Fei B. Deep learning based automatic segmentation of the placenta and uterine cavity on prenatal MR images. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2023; 12465:124650N. [PMID: 38486806 PMCID: PMC10937245 DOI: 10.1117/12.2653659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
Magnetic resonance imaging (MRI) has potential benefits in understanding fetal and placental complications in pregnancy. An accurate segmentation of the uterine cavity and placenta can help facilitate fast and automated analyses of placenta accreta spectrum and other pregnancy complications. In this study, we trained a deep neural network for fully automatic segmentation of the uterine cavity and placenta from MR images of pregnant women with and without placental abnormalities. The two datasets were axial MRI data of 241 pregnant women, among whom, 101 patients also had sagittal MRI data. Our trained model was able to perform fully automatic 3D segmentation of MR image volumes and achieved an average Dice similarity coefficient (DSC) of 92% for uterine cavity and of 82% for placenta on the sagittal dataset and an average DSC of 87% for uterine cavity and of 82% for placenta on the axial dataset. Use of our automatic segmentation method is the first step in designing an analytics tool for to assess the risk of pregnant women with placenta accreta spectrum.
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Affiliation(s)
- James Huang
- Department of Bioengineering, The University of Texas at Dallas, TX
- Center for Imaging and Surgical Innovation, The University of Texas at Dallas, TX
| | - Quyen N. Do
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Maysam Shahed
- Department of Bioengineering, The University of Texas at Dallas, TX
- Center for Imaging and Surgical Innovation, The University of Texas at Dallas, TX
| | - Yin Xi
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX
- Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Matthew A. Lewis
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Christina L. Herrera
- Department of Obstetrics and Gynecology, The University of Texas Southwestern Medical Center, Dallas, TX
| | - David Owen
- Department of Obstetrics and Gynecology, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Catherine Y. Spong
- Department of Obstetrics and Gynecology, The University of Texas Southwestern Medical Center, Dallas, TX
| | | | - Diane M. Twickler
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX
- Department of Obstetrics and Gynecology, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Baowei Fei
- Department of Bioengineering, The University of Texas at Dallas, TX
- Center for Imaging and Surgical Innovation, The University of Texas at Dallas, TX
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX
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Theis M, Tonguc T, Savchenko O, Nowak S, Block W, Recker F, Essler M, Mustea A, Attenberger U, Marinova M, Sprinkart AM. Deep learning enables automated MRI-based estimation of uterine volume also in patients with uterine fibroids undergoing high-intensity focused ultrasound therapy. Insights Imaging 2023; 14:1. [PMID: 36600120 PMCID: PMC9813298 DOI: 10.1186/s13244-022-01342-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 12/02/2022] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND High-intensity focused ultrasound (HIFU) is used for the treatment of symptomatic leiomyomas. We aim to automate uterine volumetry for tracking changes after therapy with a 3D deep learning approach. METHODS A 3D nnU-Net model in the default setting and in a modified version including convolutional block attention modules (CBAMs) was developed on 3D T2-weighted MRI scans. Uterine segmentation was performed in 44 patients with routine pelvic MRI (standard group) and 56 patients with uterine fibroids undergoing ultrasound-guided HIFU therapy (HIFU group). Here, preHIFU scans (n = 56), postHIFU imaging maximum one day after HIFU (n = 54), and the last available follow-up examination (n = 53, days after HIFU: 420 ± 377) were included. The training was performed on 80% of the data with fivefold cross-validation. The remaining data were used as a hold-out test set. Ground truth was generated by a board-certified radiologist and a radiology resident. For the assessment of inter-reader agreement, all preHIFU examinations were segmented independently by both. RESULTS High segmentation performance was already observed for the default 3D nnU-Net (mean Dice score = 0.95 ± 0.05) on the validation sets. Since the CBAM nnU-Net showed no significant benefit, the less complex default model was applied to the hold-out test set, which resulted in accurate uterus segmentation (Dice scores: standard group 0.92 ± 0.07; HIFU group 0.96 ± 0.02), which was comparable to the agreement between the two readers. CONCLUSIONS This study presents a method for automatic uterus segmentation which allows a fast and consistent assessment of uterine volume. Therefore, this method could be used in the clinical setting for objective assessment of therapeutic response to HIFU therapy.
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Affiliation(s)
- Maike Theis
- grid.15090.3d0000 0000 8786 803XDepartment of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Tolga Tonguc
- grid.15090.3d0000 0000 8786 803XDepartment of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Oleksandr Savchenko
- grid.15090.3d0000 0000 8786 803XDepartment of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Sebastian Nowak
- grid.15090.3d0000 0000 8786 803XDepartment of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Wolfgang Block
- grid.15090.3d0000 0000 8786 803XDepartment of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany ,grid.15090.3d0000 0000 8786 803XDepartment of Radiotherapy and Radiation Oncology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany ,grid.15090.3d0000 0000 8786 803XDepartment of Neuroradiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Florian Recker
- grid.15090.3d0000 0000 8786 803XDepartment of Gynaecology and Gynaecological Oncology, University Hospital Bonn, Bonn, Germany
| | - Markus Essler
- grid.15090.3d0000 0000 8786 803XDepartment of Nuclear Medicine, University Hospital Bonn, Bonn, Germany
| | - Alexander Mustea
- grid.15090.3d0000 0000 8786 803XDepartment of Gynaecology and Gynaecological Oncology, University Hospital Bonn, Bonn, Germany
| | - Ulrike Attenberger
- grid.15090.3d0000 0000 8786 803XDepartment of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Milka Marinova
- grid.15090.3d0000 0000 8786 803XDepartment of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany ,grid.15090.3d0000 0000 8786 803XDepartment of Nuclear Medicine, University Hospital Bonn, Bonn, Germany
| | - Alois M. Sprinkart
- grid.15090.3d0000 0000 8786 803XDepartment of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
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Chen X, Ming Y, Xu H, Xin Y, Yang L, Liu Z, Han Y, Huang Z, Liu Q, Zhang J. Assessment of postpartum haemorrhage for placenta accreta: Is measurement of myometrium thickness and dark intraplacental bands using MRI helpful? BMC Med Imaging 2022; 22:179. [PMID: 36253716 PMCID: PMC9575254 DOI: 10.1186/s12880-022-00906-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 10/04/2022] [Indexed: 11/10/2022] Open
Abstract
Background This study aimed to investigate the predictive values of magnetic resonance imaging (MRI) myometrial thickness grading and dark intraplacental band (DIB) volumetry for blood loss in patients with placenta accreta spectrum (PAS). Methods Images and clinical data were acquired from patients who underwent placenta MRI examinations and were diagnosed with PAS from March 2015 to January 2021. Two radiologists jointly diagnosed, processed, and analysed the MR images of each patient. The analysis included MRI-based determination of placental attachment, as well as myometrial thickness grading and DIB volumetry. The patients included in the study were divided into three groups according to the estimated blood loss volume: in the general blood loss (GBL) group, the estimated blood loss volume was < 1000 ml; in the massive blood loss (MBL) group, the estimated blood loss volume was ≥ 1000 ml and < 2000 ml; and in the extremely massive blood loss (ex-MBL) group, the estimated blood loss volume was ≥ 2000 ml. The categorical, normally distributed, and non-normally distributed data were respectively analysed by the Chi-square, single-factor analysis of variance, and Kruskal–Wallis tests, respectively. The verification of correlation was completed by Spearman correlation analysis. The evaluation capabilities of indicators were assessed using receiver operating characteristic curves. Results Among 75 patients, 25 were included in the GBL group, 26 in the MBL group, and 24 in the ex-MBL group. A significant negative correlation was observed between the grade of myometrial thickness and the estimated blood loss (P < 0.001, ρ = − 0.604). There was a significant positive correlation between the volume of the DIB and the estimated blood loss (P < 0.001, ρ = 0.653). The areas under the receiver operating characteristic curve of the two MRI features for predicting blood loss ≥ 2000 ml were 0.776 and 0.897, respectively. Conclusions The grading and volumetric MRI features, myometrial thickness, and volume of DIB, can be used as good prediction indicators of the risk of postpartum haemorrhage in patients with PAS.
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Affiliation(s)
- Xinyi Chen
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, China
| | - Ying Ming
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324, Jingwu Road, Huaiyin District, Jinan, 250012, Shandong, China.,Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No. 1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Han Xu
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324, Jingwu Road, Huaiyin District, Jinan, 250012, Shandong, China
| | - Yinghui Xin
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324, Jingwu Road, Huaiyin District, Jinan, 250012, Shandong, China
| | - Lin Yang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324, Jingwu Road, Huaiyin District, Jinan, 250012, Shandong, China
| | - Zhiling Liu
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324, Jingwu Road, Huaiyin District, Jinan, 250012, Shandong, China
| | - Yuqing Han
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, China
| | - Zhaoqin Huang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324, Jingwu Road, Huaiyin District, Jinan, 250012, Shandong, China
| | - Qingwei Liu
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, China.,Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324, Jingwu Road, Huaiyin District, Jinan, 250012, Shandong, China
| | - Jie Zhang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324, Jingwu Road, Huaiyin District, Jinan, 250012, Shandong, China.
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Naftali S, Ashkenazi YN, Ratnovsky A. A novel approach based on machine learning analysis of flow velocity waveforms to identify unseen abnormalities of the umbilical cord. Placenta 2022; 127:20-28. [DOI: 10.1016/j.placenta.2022.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/13/2022] [Accepted: 07/14/2022] [Indexed: 11/24/2022]
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Shahedi M, Dormer JD, Do QN, Xi Y, Lewis MA, Herrera CL, Spong CY, Madhuranthakam AJ, Twickler DM, Fei B. Automatic Segmentation of Uterine Cavity and Placenta on MR Images Using Deep Learning. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12036:1203611. [PMID: 36798450 PMCID: PMC9929634 DOI: 10.1117/12.2613286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Magnetic resonance imaging (MRI) is useful for the detection of abnormalities affecting maternal and fetal health. In this study, we used a fully convolutional neural network for simultaneous segmentation of the uterine cavity and placenta on MR images. We trained the network with MR images of 181 patients, with 157 for training and 24 for validation. The segmentation performance of the algorithm was evaluated using MR images of 60 additional patients that were not involved in training. The average Dice similarity coefficients achieved for the uterine cavity and placenta were 92% and 80%, respectively. The algorithm could estimate the volume of the uterine cavity and placenta with average errors of less than 1.1% compared to manual estimations. Automated segmentation, when incorporated into clinical use, has the potential to quantify, standardize, and improve placental assessment, resulting in improved outcomes for mothers and fetuses.
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Affiliation(s)
- Maysam Shahedi
- Department of Bioengineering, The University of Texas at Dallas, TX
- Center for Imaging and Surgical Innovation, The University of Texas at Dallas, TX
| | - James D. Dormer
- Department of Bioengineering, The University of Texas at Dallas, TX
- Center for Imaging and Surgical Innovation, The University of Texas at Dallas, TX
| | - Quyen N. Do
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Yin Xi
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX
- Department of Clinical Science, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Matthew A. Lewis
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Christina L. Herrera
- Department of Obstetrics and Gynecology, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Catherine Y. Spong
- Department of Obstetrics and Gynecology, The University of Texas Southwestern Medical Center, Dallas, TX
| | | | - Diane M. Twickler
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX
- Department of Obstetrics and Gynecology, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Baowei Fei
- Department of Bioengineering, The University of Texas at Dallas, TX
- Center for Imaging and Surgical Innovation, The University of Texas at Dallas, TX
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX
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10
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Leitch K, Shahedi M, Dormer JD, Do QN, Xi Y, Lewis MA, Herrera CL, Spong CY, Madhuranthakam AJ, Twickler DM, Fei B. Placenta Accreta Spectrum and Hysterectomy Prediction Using MRI Radiomic Features. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12033:120331I. [PMID: 36844110 PMCID: PMC9956938 DOI: 10.1117/12.2611587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
In women with placenta accreta spectrum (PAS), patient management may involve cesarean hysterectomy at delivery. Magnetic resonance imaging (MRI) has been used for further evaluation of PAS and surgical planning. This work tackles two prediction problems: predicting presence of PAS and predicting hysterectomy using MR images of pregnant patients. First, we extracted approximately 2,500 radiomic features from MR images with two regions of interest: the placenta and the uterus. In addition to analyzing two regions of interest, we dilated the placenta and uterus masks by 5, 10, 15, and 20 mm to gain insights from the myometrium, where the uterus and placenta overlap in the case of PAS. This study cohort includes 241 pregnant women. Of these women, 89 underwent hysterectomy while 152 did not; 141 with suspected PAS, and 100 without suspected PAS. We obtained an accuracy of 0.88 for predicting hysterectomy and an accuracy of 0.92 for classifying suspected PAS. The radiomic analysis tool is further validated, it can be useful for aiding clinicians in decision making on the care of pregnant women.
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Affiliation(s)
- Ka’Toria Leitch
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX
| | - Maysam Shahedi
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX
| | - James D. Dormer
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX
| | - Quyen N. Do
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Yin Xi
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX
- Department of Clinical Science, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Matthew A. Lewis
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Christina L. Herrera
- Department of Obstetrics and Gynecology, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Catherine Y. Spong
- Department of Obstetrics and Gynecology, The University of Texas Southwestern Medical Center, Dallas, TX
| | | | - Diane M. Twickler
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX
- Department of Obstetrics and Gynecology, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Baowei Fei
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX
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