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Sivera R, Clark AE, Dall'Asta A, Ghi T, Schievano S, Lees CC. Fetal face shape analysis from prenatal 3D ultrasound images. Sci Rep 2024; 14:4411. [PMID: 38388522 PMCID: PMC10884000 DOI: 10.1038/s41598-023-50386-9] [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: 06/26/2023] [Accepted: 12/19/2023] [Indexed: 02/24/2024] Open
Abstract
3D ultrasound imaging of fetal faces has been predominantly confined to qualitative assessment. Many genetic conditions evade diagnosis and identification could assist with parental counselling, pregnancy management and neonatal care planning. We describe a methodology to build a shape model of the third trimester fetal face from 3D ultrasound and show how it can objectively describe morphological features and gestational-age related changes of normal fetal faces. 135 fetal face 3D ultrasound volumes (117 appropriately grown, 18 growth-restricted) of 24-34 weeks gestation were included. A 3D surface model of each face was obtained using a semi-automatic segmentation workflow. Size normalisation and rescaling was performed using a growth model giving the average size at every gestation. The model demonstrated a similar growth rate to standard head circumference reference charts. A landmark-free morphometry model was estimated to characterize shape differences using non-linear deformations of an idealized template face. Advancing gestation is associated with widening/fullness of the cheeks, contraction of the chin and deepening of the eyes. Fetal growth restriction is associated with a smaller average facial size but no morphological differences. This model may eventually be used as a reference to assist in the prenatal diagnosis of congenital anomalies with characteristic facial dysmorphisms.
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Affiliation(s)
- Raphael Sivera
- Institute of Cardiovascular Science, University College London, London, UK
| | - Anna E Clark
- Institute of Reproductive and Development Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Andrea Dall'Asta
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Tullio Ghi
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Silvia Schievano
- Institute of Cardiovascular Science, University College London, London, UK
| | - Christoph C Lees
- Institute of Reproductive and Development Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
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Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities. SENSORS 2022; 22:s22124570. [PMID: 35746352 PMCID: PMC9228529 DOI: 10.3390/s22124570] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 06/13/2022] [Accepted: 06/14/2022] [Indexed: 12/13/2022]
Abstract
A fetal ultrasound (US) is a technique to examine a baby’s maturity and development. US examinations have varying purposes throughout pregnancy. Consequently, in the second and third trimester, US tests are performed for the assessment of Amniotic Fluid Volume (AFV), a key indicator of fetal health. Disorders resulting from abnormal AFV levels, commonly referred to as oligohydramnios or polyhydramnios, may pose a serious threat to a mother’s or child’s health. This paper attempts to accumulate and compare the most recent advancements in Artificial Intelligence (AI)-based techniques for the diagnosis and classification of AFV levels. Additionally, we provide a thorough and highly inclusive breakdown of other relevant factors that may cause abnormal AFV levels, including, but not limited to, abnormalities in the placenta, kidneys, or central nervous system, as well as other contributors, such as preterm birth or twin-to-twin transfusion syndrome. Furthermore, we bring forth a concise overview of all the Machine Learning (ML) and Deep Learning (DL) techniques, along with the datasets supplied by various researchers. This study also provides a brief rundown of the challenges and opportunities encountered in this field, along with prospective research directions and promising angles to further explore.
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Clark AE, Biffi B, Sivera R, Dall'Asta A, Fessey L, Wong TL, Paramasivam G, Dunaway D, Schievano S, Lees CC. Developing and testing an algorithm for automatic segmentation of the fetal face from three-dimensional ultrasound images. ROYAL SOCIETY OPEN SCIENCE 2020; 7:201342. [PMID: 33391808 PMCID: PMC7735327 DOI: 10.1098/rsos.201342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 10/06/2020] [Indexed: 06/12/2023]
Abstract
Fetal craniofacial abnormalities are challenging to detect and diagnose on prenatal ultrasound (US). Image segmentation and computer analysis of three-dimensional US volumes of the fetal face may provide an objective measure to quantify fetal facial features and identify abnormalities. We have developed and tested an atlas-based partially automated facial segmentation algorithm; however, the volumes require additional manual segmentation (MS), which is time and labour intensive and may preclude this method from clinical adoption. These manually refined segmentations can then be used as a reference (atlas) by the partially automated segmentation algorithm to improve algorithmic performance with the aim of eliminating the need for manual refinement and developing a fully automated system. This study assesses the inter- and intra-operator variability of MS and tests an optimized version of our automatic segmentation (AS) algorithm. The manual refinements of 15 fetal faces performed by three operators and repeated by one operator were assessed by Dice score, average symmetrical surface distance and volume difference. The performance of the partially automatic algorithm with difference size atlases was evaluated by Dice score and computational time. Assessment of the manual refinements showed low inter- and intra-operator variability demonstrating its suitability for optimizing the AS algorithm. The algorithm showed improved performance following an increase in the atlas size in turn reducing the need for manual refinement.
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Affiliation(s)
- A. E. Clark
- Queen Charlotte's and Chelsea Hospital, Imperial Healthcare NHS Trust, London, UK
- Imperial College London, London, UK
| | - B. Biffi
- Imperial College London, London, UK
| | | | - A. Dall'Asta
- Queen Charlotte's and Chelsea Hospital, Imperial Healthcare NHS Trust, London, UK
- Imperial College London, London, UK
- Department of Medicine and Surgery, Obstetrics and Gynaecology Unit, University of Parma, Italy
| | | | - T.-L. Wong
- Queen Charlotte's and Chelsea Hospital, Imperial Healthcare NHS Trust, London, UK
| | - G. Paramasivam
- Queen Charlotte's and Chelsea Hospital, Imperial Healthcare NHS Trust, London, UK
- Imperial College London, London, UK
| | - D. Dunaway
- University College London GOS Institute of Child Health, London, UK
- Great Ormond Street Hospital for Children, London, UK
| | - S. Schievano
- University College London GOS Institute of Child Health, London, UK
- Great Ormond Street Hospital for Children, London, UK
| | - C. C. Lees
- Queen Charlotte's and Chelsea Hospital, Imperial Healthcare NHS Trust, London, UK
- Institute of Reproductive and Developmental Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
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Yang X, Yu L, Li S, Wen H, Luo D, Bian C, Qin J, Ni D, Heng PA. Towards Automated Semantic Segmentation in Prenatal Volumetric Ultrasound. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:180-193. [PMID: 30040635 DOI: 10.1109/tmi.2018.2858779] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Volumetric ultrasound is rapidly emerging as a viable imaging modality for routine prenatal examinations. Biometrics obtained from the volumetric segmentation shed light on the reformation of precise maternal and fetal health monitoring. However, the poor image quality, low contrast, boundary ambiguity, and complex anatomy shapes conspire toward a great lack of efficient tools for the segmentation. It makes 3-D ultrasound difficult to interpret and hinders the widespread of 3-D ultrasound in obstetrics. In this paper, we are looking at the problem of semantic segmentation in prenatal ultrasound volumes. Our contribution is threefold: 1) we propose the first and fully automatic framework to simultaneously segment multiple anatomical structures with intensive clinical interest, including fetus, gestational sac, and placenta, which remains a rarely studied and arduous challenge; 2) we propose a composite architecture for dense labeling, in which a customized 3-D fully convolutional network explores spatial intensity concurrency for initial labeling, while a multi-directional recurrent neural network (RNN) encodes spatial sequentiality to combat boundary ambiguity for significant refinement; and 3) we introduce a hierarchical deep supervision mechanism to boost the information flow within RNN and fit the latent sequence hierarchy in fine scales, and further improve the segmentation results. Extensively verified on in-house large data sets, our method illustrates a superior segmentation performance, decent agreements with expert measurements and high reproducibilities against scanning variations, and thus is promising in advancing the prenatal ultrasound examinations.
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Torrents-Barrena J, Piella G, Masoller N, Gratacós E, Eixarch E, Ceresa M, Ballester MÁG. Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects. Med Image Anal 2018; 51:61-88. [PMID: 30390513 DOI: 10.1016/j.media.2018.10.003] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2017] [Revised: 10/09/2018] [Accepted: 10/18/2018] [Indexed: 12/19/2022]
Abstract
Fetal imaging is a burgeoning topic. New advancements in both magnetic resonance imaging and (3D) ultrasound currently allow doctors to diagnose fetal structural abnormalities such as those involved in twin-to-twin transfusion syndrome, gestational diabetes mellitus, pulmonary sequestration and hypoplasia, congenital heart disease, diaphragmatic hernia, ventriculomegaly, etc. Considering the continued breakthroughs in utero image analysis and (3D) reconstruction models, it is now possible to gain more insight into the ongoing development of the fetus. Best prenatal diagnosis performances rely on the conscious preparation of the clinicians in terms of fetal anatomy knowledge. Therefore, fetal imaging will likely span and increase its prevalence in the forthcoming years. This review covers state-of-the-art segmentation and classification methodologies for the whole fetus and, more specifically, the fetal brain, lungs, liver, heart and placenta in magnetic resonance imaging and (3D) ultrasound for the first time. Potential applications of the aforementioned methods into clinical settings are also inspected. Finally, improvements in existing approaches as well as most promising avenues to new areas of research are briefly outlined.
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Affiliation(s)
- Jordina Torrents-Barrena
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Gemma Piella
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Narcís Masoller
- BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), IDIBAPS, University of Barcelona, Barcelona, Spain; Center for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - Eduard Gratacós
- BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), IDIBAPS, University of Barcelona, Barcelona, Spain; Center for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - Elisenda Eixarch
- BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), IDIBAPS, University of Barcelona, Barcelona, Spain; Center for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - Mario Ceresa
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Miguel Ángel González Ballester
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; ICREA, Barcelona, Spain
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Marsousi M, Plataniotis KN, Stergiopoulos S. Kidney Detection in 3-D Ultrasound Imagery via Shape-to-Volume Registration Based on Spatially Aligned Neural Network. IEEE J Biomed Health Inform 2018; 23:227-242. [PMID: 29993823 DOI: 10.1109/jbhi.2018.2805777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This paper introduces a computer-aided kidney shape detection method suitable for volumetric (3D) ultrasound images. Using shape and texture priors, the proposed method automates the process of kidney detection, which is a problem of great importance in computer-assisted trauma diagnosis. This paper introduces a new complex-valued implicit shape model, which represents the multiregional structure of the kidney shape. A spatially aligned neural network classifiers with complex-valued output is designed to classify voxels into background and multiregional structure of the kidney shape. The complex values of the shape model and classification outputs are selected and incorporated in a new similarity metric, such as the shape-to-volume registration process only fits the shape model on the actual kidney shape in input ultrasound volumes. The algorithm's accuracy and sensitivity are evaluated using both simulated and actual 3-D ultrasound images, and it is compared against the performance of the state of the art. The results support the claims about accuracy and robustness of the proposed kidney detection method, and statistical analysis validates its superiority over the state of the art.
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Automated Techniques for the Interpretation of Fetal Abnormalities: A Review. Appl Bionics Biomech 2018; 2018:6452050. [PMID: 29983738 PMCID: PMC6015700 DOI: 10.1155/2018/6452050] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Revised: 04/07/2018] [Accepted: 05/10/2018] [Indexed: 11/17/2022] Open
Abstract
Ultrasound (US) image segmentation methods, focusing on techniques developed for fetal biometric parameters and nuchal translucency, are briefly reviewed. Ultrasound medical images can easily identify the fetus using segmentation techniques and calculate fetal parameters. It can timely find the fetal abnormality so that necessary action can be taken by the pregnant woman. Firstly, a detailed literature has been offered on fetal biometric parameters and nuchal translucency to highlight the investigation approaches with a degree of validation in diverse clinical domains. Then, a categorization of the bibliographic assessment of recent research effort in the segmentation field of ultrasound 2D fetal images has been presented. The fetal images of high-risk pregnant women have been taken into the routine and continuous monitoring of fetal parameters. These parameters are used for detection of fetal weight, fetal growth, gestational age, and any possible abnormality detection.
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Marsousi M, Plataniotis KN, Stergiopoulos S. An Automated Approach for Kidney Segmentation in Three-Dimensional Ultrasound Images. IEEE J Biomed Health Inform 2017; 21:1079-1094. [DOI: 10.1109/jbhi.2016.2580040] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Sciolla B, Cowell L, Dambry T, Guibert B, Delachartre P. Segmentation of Skin Tumors in High-Frequency 3-D Ultrasound Images. ULTRASOUND IN MEDICINE & BIOLOGY 2017; 43:227-238. [PMID: 27720519 DOI: 10.1016/j.ultrasmedbio.2016.08.029] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Revised: 07/21/2016] [Accepted: 08/29/2016] [Indexed: 06/06/2023]
Abstract
High-frequency 3-D ultrasound imaging is an informative tool for diagnosis, surgery planning and skin lesion examination. The purpose of this article was to describe a semi-automated segmentation tool providing easy access to the extent, shape and volume of a lesion. We propose an adaptive log-likelihood level-set segmentation procedure using non-parametric estimates of the intensity distribution. The algorithm has a single parameter to control the smoothness of the contour, and we describe how a fixed value yields satisfactory segmentation results with an average Dice coefficient of D = 0.76. The algorithm is implemented on a grid, which increases the speed by a factor of 100 compared with a standard pixelwise segmentation. We compare the method with parametric methods making the hypothesis of Rayleigh or Nakagami distributed signals, and illustrate that our method has greater robustness with similar computational speed. Benchmarks are made on realistic synthetic ultrasound images and a data set of nine clinical 3-D images acquired with a 50-MHz imaging system. The proposed algorithm is suitable for use in a clinical context as a post-processing tool.
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Affiliation(s)
| | - Lester Cowell
- Melanoma Skin Cancer Clinic, Hamilton Hill, Western Australia, Australia
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Bonacina L, Froio A, Conti D, Marcolin F, Vezzetti E. Automatic 3D foetal face model extraction from ultrasonography through histogram processing. J Med Ultrasound 2016. [DOI: 10.1016/j.jmu.2016.08.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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Coron A, Mamou J, Saegusa-Beecroft E, Yamaguchi T, Yanagihara E, Machi J, Bridal SL, Feleppa EJ. Local Transverse-Slice-Based Level-Set Method for Segmentation of 3-D High-Frequency Ultrasonic Backscatter From Dissected Human Lymph Nodes. IEEE Trans Biomed Eng 2016; 64:1579-1591. [PMID: 28113305 DOI: 10.1109/tbme.2016.2614137] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE To detect metastases in freshly excised human lymph nodes (LNs) using three-dimensional (3-D), high-frequency, quantitative ultrasound (QUS) methods, the LN parenchyma (LNP) must be segmented to preclude QUS analysis of data in regions outside the LNP and to compensate ultrasound attenuation effects due to overlying layers of LNP and residual perinodal fat (PNF). METHODS After restoring the saturated radio-frequency signals from PNF using an approach based on smoothing cubic splines, the three regions, i.e., LNP, PNF, and normal saline (NS), in the LN envelope data are segmented using a new, automatic, 3-D, three-phase, statistical transverseslice-based level-set (STS-LS) method that amends Lankton's method. Due to ultrasound attenuation and focusing effects, the speckle statistics of the envelope data vary with imaged depth. Thus, to mitigate depth-related inhomogeneity effects, the STS-LS method employs gamma probabilitydensity functions to locally model the speckle statistics within consecutive transverse slices. RESULTS Accurate results were obtained on simulated data. On a representative dataset of 54 LNs acquired from colorectal-cancer patients, the Dice similarity coefficient for LNP, PNF, and NS were 0.938 ± 0.025, 0.832 ± 0.086, and 0.968 ± 0.008, respectively, when compared to expert manual segmentation. CONCLUSION The STS-LS outperforms the established methods based on global and local statistics in our datasets and is capable of accurately handling the depth-dependent effects due to attenuation and focusing. SIGNIFICANCE This advance permits the automatic QUS-based cancer detection in the LNs. Furthermore, the STS-LS method could potentially be used in a wide range of ultrasound-imaging applications suffering from depth-dependent effects.
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Dahdouh S, Angelini ED, Grangé G, Bloch I. Segmentation of embryonic and fetal 3D ultrasound images based on pixel intensity distributions and shape priors. Med Image Anal 2015; 24:255-268. [DOI: 10.1016/j.media.2014.12.005] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2014] [Revised: 12/16/2014] [Accepted: 12/18/2014] [Indexed: 12/26/2022]
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Varsier N, Dahdouh S, Serrurier A, De la Plata JP, Anquez J, Angelini ED, Bloch I, Wiart J. Influence of pregnancy stage and fetus position on the whole-body and local exposure of the fetus to RF-EMF. Phys Med Biol 2014; 59:4913-26. [PMID: 25098501 DOI: 10.1088/0031-9155/59/17/4913] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
This paper analyzes the influence of pregnancy stage and fetus position on the whole-body and brain exposure of the fetus to radiofrequency electromagnetic fields. Our analysis is performed using semi-homogeneous pregnant woman models between 8 and 32 weeks of amenorrhea. By analyzing the influence of the pregnancy stage on the environmental whole-body and local exposure of a fetus in vertical position, head down or head up, in the 2100 MHz frequency band, we concluded that both whole-body and average brain exposures of the fetus decrease during the first pregnancy trimester, while they advance during the pregnancy due to the rapid weight gain of the fetus in these first stages. From the beginning of the second trimester, the whole-body and the average brain exposures are quite stable because the weight gains are quasi proportional to the absorbed power increases. The behavior of the fetus whole-body and local exposures during pregnancy for a fetus in the vertical position with the head up were found to be of a similar level, when compared to the position with the head down they were slightly higher, especially in the brain.
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Affiliation(s)
- N Varsier
- Orange, Issy les Moulineaux, France. WHIST Joint Laboratory between Institut Mines-Telecom and Orange, France
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Dahdouh S, Varsier N, Serrurier A, De la Plata JP, Anquez J, Angelini ED, Wiart J, Bloch I. A comprehensive tool for image-based generation of fetus and pregnant women mesh models for numerical dosimetry studies. Phys Med Biol 2014; 59:4583-602. [PMID: 25079007 DOI: 10.1088/0031-9155/59/16/4583] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Fetal dosimetry studies require the development of accurate numerical 3D models of the pregnant woman and the fetus. This paper proposes a 3D articulated fetal growth model covering the main phases of pregnancy and a pregnant woman model combining the utero-fetal structures and a deformable non-pregnant woman body envelope. The structures of interest were automatically or semi-automatically (depending on the stage of pregnancy) segmented from a database of images and surface meshes were generated. By interpolating linearly between fetal structures, each one can be generated at any age and in any position. A method is also described to insert the utero-fetal structures in the maternal body. A validation of the fetal models is proposed, comparing a set of biometric measurements to medical reference charts. The usability of the pregnant woman model in dosimetry studies is also investigated, with respect to the influence of the abdominal fat layer.
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Affiliation(s)
- S Dahdouh
- Institut Mines-Telecom, Telecom ParisTech, CNRS LTCI, Paris, France. Whist Lab, Paris, France
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Bui TM, Coron A, Mamou J, Saegusa-Beecroft E, Yamaguchi T, Yanagihara E, Machi J, Bridal SL, Feleppa EJ. Modeling the envelope statistics of three-dimensional high-frequency ultrasound echo signals from dissected human lymph nodes. JAPANESE JOURNAL OF APPLIED PHYSICS (2008) 2014; 53:07KF22. [PMID: 25346951 PMCID: PMC4208025 DOI: 10.7567/jjap.53.07kf22] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
This work investigates the statistics of the envelope of three-dimensional (3D) high-frequency ultrasound (HFU) data acquired from dissected human lymph nodes (LNs). Nine distributions were employed, and their parameters were estimated using the method of moments. The Kolmogorov Smirnov (KS) metric was used to quantitatively compare the fit of each candidate distribution to the experimental envelope distribution. The study indicates that the generalized gamma distribution best models the statistics of the envelope data of the three media encountered: LN parenchyma, fat and phosphate-buffered saline (PBS). Furthermore, the envelope statistics of the LN parenchyma satisfy the pre-Rayleigh condition. In terms of high fitting accuracy and computationally efficient parameter estimation, the gamma distribution is the best choice to model the envelope statistics of LN parenchyma, while, the Weibull distribution is the best choice to model the envelope statistics of fat and PBS. These results will contribute to the development of more-accurate and automatic 3D segmentation of LNs for ultrasonic detection of clinically significant LN metastases.
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Affiliation(s)
- Thanh Minh Bui
- Sorbonne Universités, UPMC Univ. Paris 06, UMR 7371, UMR_S 1146, Laboratoire d'Imagerie Biomédicale, F-75006, Paris, France
- CNRS, UMR 7371, Laboratoire d'Imagerie Biomédicale, F-75006, Paris, France
- INSERM, UMR_S 1146, Laboratoire d'Imagerie Biomédicale, F-75006, Paris, France
| | - Alain Coron
- Sorbonne Universités, UPMC Univ. Paris 06, UMR 7371, UMR_S 1146, Laboratoire d'Imagerie Biomédicale, F-75006, Paris, France
- CNRS, UMR 7371, Laboratoire d'Imagerie Biomédicale, F-75006, Paris, France
- INSERM, UMR_S 1146, Laboratoire d'Imagerie Biomédicale, F-75006, Paris, France
| | - Jonathan Mamou
- F. L. Lizzi Center for Biomedical Engineering, Riverside Research, New York, NY 10038, U.S.A
| | - Emi Saegusa-Beecroft
- Department of Surgery, University of Hawaii and Kuakini Medical Center, Honolulu, HI 96817, U.S.A
| | - Tadashi Yamaguchi
- Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Japan
| | - Eugene Yanagihara
- Department of Pathology, Kuakini Medical Center, Honolulu, HI 96817, U.S.A
| | - Junji Machi
- Department of Surgery, University of Hawaii and Kuakini Medical Center, Honolulu, HI 96817, U.S.A
| | - S. Lori Bridal
- Sorbonne Universités, UPMC Univ. Paris 06, UMR 7371, UMR_S 1146, Laboratoire d'Imagerie Biomédicale, F-75006, Paris, France
- CNRS, UMR 7371, Laboratoire d'Imagerie Biomédicale, F-75006, Paris, France
- INSERM, UMR_S 1146, Laboratoire d'Imagerie Biomédicale, F-75006, Paris, France
| | - Ernest J. Feleppa
- F. L. Lizzi Center for Biomedical Engineering, Riverside Research, New York, NY 10038, U.S.A
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