1
|
Cornelissen S, Schouten SM, Langenhuizen PPJH, Lie ST, Kunst HPM, de With PHN, Verheul JB. Defining tumor growth in vestibular schwannomas: a volumetric inter-observer variability study in contrast-enhanced T1-weighted MRI. Neuroradiology 2024:10.1007/s00234-024-03416-w. [PMID: 38980343 DOI: 10.1007/s00234-024-03416-w] [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: 04/10/2024] [Accepted: 06/25/2024] [Indexed: 07/10/2024]
Abstract
PURPOSE For patients with vestibular schwannomas (VS), a conservative observational approach is increasingly used. Therefore, the need for accurate and reliable volumetric tumor monitoring is important. Currently, a volumetric cutoff of 20% increase in tumor volume is widely used to define tumor growth in VS. The study investigates the tumor volume dependency on the limits of agreement (LoA) for volumetric measurements of VS by means of an inter-observer study. METHODS This retrospective study included 100 VS patients who underwent contrast-enhanced T1-weighted MRI. Five observers volumetrically annotated the images. Observer agreement and reliability was measured using the LoA, estimated using the limits of agreement with the mean (LOAM) method, and the intraclass correlation coefficient (ICC). RESULTS The 100 patients had a median average tumor volume of 903 mm3 (IQR: 193-3101). Patients were divided into four volumetric size categories based on tumor volume quartile. The smallest tumor volume quartile showed a LOAM relative to the mean of 26.8% (95% CI: 23.7-33.6), whereas for the largest tumor volume quartile this figure was found to be 7.3% (95% CI: 6.5-9.7) and when excluding peritumoral cysts: 4.8% (95% CI: 4.2-6.2). CONCLUSION Agreement limits within volumetric annotation of VS are affected by tumor volume, since the LoA improves with increasing tumor volume. As a result, for tumors larger than 200 mm3, growth can reliably be detected at an earlier stage, compared to the currently widely used cutoff of 20%. However, for very small tumors, growth should be assessed with higher agreement limits than previously thought.
Collapse
Affiliation(s)
- Stefan Cornelissen
- Gamma Knife Center, Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands.
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
| | - Sammy M Schouten
- Gamma Knife Center, Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands
- Department of Otolaryngology, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Otolaryngology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Patrick P J H Langenhuizen
- Gamma Knife Center, Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Suan Te Lie
- Gamma Knife Center, Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands
| | - Henricus P M Kunst
- Department of Otolaryngology, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Otolaryngology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Peter H N de With
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Jeroen B Verheul
- Gamma Knife Center, Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands
| |
Collapse
|
2
|
Kobets AJ, Alavi SAN, Ahmad SJ, Castillo A, Young D, Minuti A, Altschul DJ, Zhu M, Abbott R. Volumetric segmentation in the context of posterior fossa-related pathologies: a systematic review. Neurosurg Rev 2024; 47:170. [PMID: 38637466 PMCID: PMC11026186 DOI: 10.1007/s10143-024-02366-4] [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: 01/28/2024] [Revised: 03/04/2024] [Accepted: 03/16/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND Segmentation tools continue to advance, evolving from manual contouring to deep learning. Researchers have utilized segmentation to study a myriad of posterior fossa-related conditions, such as Chiari malformation, trigeminal neuralgia, post-operative pediatric cerebellar mutism syndrome, and Crouzon syndrome. Herein, we present a summary of the current literature on segmentation of the posterior fossa. The review highlights the various segmentation techniques, and their respective strengths and weaknesses, employed along with objectives and outcomes of the various studies reported in the literature. METHODS A literature search was conducted in PubMed, Embase, Cochrane, and Web of Science up to November 2023 for articles on segmentation techniques of posterior fossa. The two senior authors searched through databases based on the keywords of the article separately and then enrolled joint articles that met the inclusion and exclusion criteria. RESULTS The initial search identified 2205 articles. After applying inclusion and exclusion criteria, 77 articles were selected for full-text review after screening of titles/abstracts. 52 articles were ultimately included in the review. Segmentation techniques included manual, semi-automated, and fully automated (atlas-based, convolutional neural networks). The most common pathology investigated was Chiari malformation. CONCLUSIONS Various forms of segmentation techniques have been used to assess posterior fossa volumes/pathologies and each has its advantages and disadvantages. We discuss these nuances and summarize the current state of literature in the context of posterior fossa-associated pathologies.
Collapse
Affiliation(s)
- Andrew J Kobets
- Department of Neurological Surgery, Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, NY, 10467, USA
| | - Seyed Ahmad Naseri Alavi
- Department of Neurological Surgery, Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, NY, 10467, USA.
| | | | | | | | | | - David J Altschul
- Department of Neurological Surgery, Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, NY, 10467, USA
| | - Michael Zhu
- Albert Einstein College of Medicine, New York City, USA
| | - Rick Abbott
- Department of Neurological Surgery, Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, NY, 10467, USA
| |
Collapse
|
3
|
Drossopoulos PN, Ononogbu-uche FC, Tabarestani TQ, Huang CC, Paturu M, Bardeesi A, Ray WZ, Shaffrey CI, Goodwin CR, Erickson M, Chi JH, Abd-El-Barr MM. Evolution of the Transforaminal Lumbar Interbody Fusion (TLIF): From Open to Percutaneous to Patient-Specific. J Clin Med 2024; 13:2271. [PMID: 38673544 PMCID: PMC11051479 DOI: 10.3390/jcm13082271] [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: 02/21/2024] [Revised: 04/04/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
The transforaminal lumbar interbody fusion (TLIF) has seen significant evolution since its early inception, reflecting advancements in surgical techniques, patient safety, and outcomes. Originally described as an improvement over the posterior lumbar interbody fusion (PLIF), the TLIF began as an open surgical procedure, that notably reduced the need for the extensive neural retractation that hindered the PLIF. In line with the broader practice of surgery, trending toward minimally invasive access, the TLIF was followed by the development of the minimally invasive TLIF (MIS-TLIF), a technique that further decreased tissue trauma and postoperative complications. Subsequent advancements, including Trans-Kambin's Triangle TLIF (percLIF) and transfacet LIF, have continued to refine surgical access, minimize surgical footprint, and reduce the risk of injury to the patient. The latest evolution, as we will describe it, the patient-specific TLIF, is a culmination of the aforementioned adaptations and incorporates advanced imaging and segmentation technologies into perioperative planning, allowing surgeons to tailor approaches based on individual patient anatomy and pathology. These developments signify a shift towards more precise methods in spine surgery. The ongoing evolution of the TLIF technique illustrates the dynamic nature of surgery and emphasizes the need for continued adaptation and refinement.
Collapse
Affiliation(s)
- Peter N. Drossopoulos
- Division of Spine, Department of Neurosurgery, Duke University, Durham, NC 27710, USA (T.Q.T.); (M.M.A.-E.-B.)
| | - Favour C. Ononogbu-uche
- Division of Spine, Department of Neurosurgery, Duke University, Durham, NC 27710, USA (T.Q.T.); (M.M.A.-E.-B.)
| | - Troy Q. Tabarestani
- Division of Spine, Department of Neurosurgery, Duke University, Durham, NC 27710, USA (T.Q.T.); (M.M.A.-E.-B.)
| | - Chuan-Ching Huang
- Division of Spine, Department of Neurosurgery, Duke University, Durham, NC 27710, USA (T.Q.T.); (M.M.A.-E.-B.)
| | - Mounica Paturu
- Division of Spine, Department of Neurosurgery, Duke University, Durham, NC 27710, USA (T.Q.T.); (M.M.A.-E.-B.)
| | - Anas Bardeesi
- Division of Spine, Department of Neurosurgery, Duke University, Durham, NC 27710, USA (T.Q.T.); (M.M.A.-E.-B.)
| | - Wilson Z. Ray
- Department of Neurological Surgery, Washington University, St Louis, MO 63110, USA
| | - Christopher I. Shaffrey
- Division of Spine, Department of Neurosurgery, Duke University, Durham, NC 27710, USA (T.Q.T.); (M.M.A.-E.-B.)
| | - C. Rory Goodwin
- Division of Spine, Department of Neurosurgery, Duke University, Durham, NC 27710, USA (T.Q.T.); (M.M.A.-E.-B.)
| | - Melissa Erickson
- Division of Spine, Department of Orthopedic Surgery, Duke University Medical Center, Durham, NC 27710, USA
| | - John H. Chi
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Muhammad M. Abd-El-Barr
- Division of Spine, Department of Neurosurgery, Duke University, Durham, NC 27710, USA (T.Q.T.); (M.M.A.-E.-B.)
| |
Collapse
|
4
|
Suresh K, Luo G, Bartholomew RA, Brown A, Juliano AF, Lee DJ, Welling DB, Cai W, Crowson MG. An External Validation Study for Automated Segmentation of Vestibular Schwannoma. Otol Neurotol 2024; 45:e193-e197. [PMID: 38361299 DOI: 10.1097/mao.0000000000004125] [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: 02/17/2024]
Abstract
OBJECTIVE To validate how an automated model for vestibular schwannoma (VS) segmentation developed on an external homogeneous dataset performs when applied to internal heterogeneous data. PATIENTS The external dataset comprised 242 patients with previously untreated, sporadic unilateral VS undergoing Gamma Knife radiosurgery, with homogeneous magnetic resonance imaging (MRI) scans. The internal dataset comprised 10 patients from our institution, with heterogeneous MRI scans. INTERVENTIONS An automated VS segmentation model was developed on the external dataset. The model was tested on the internal dataset. MAIN OUTCOME MEASURE Dice score, which measures agreement between ground truth and predicted segmentations. RESULTS When applied to the internal patient scans, the automated model achieved a mean Dice score of 61% across all 10 images. There were three tumors that were not detected. These tumors were 0.01 ml on average (SD = 0.00 ml). The mean Dice score for the seven tumors that were detected was 87% (SD = 14%). There was one outlier with Dice of 55%-on further review of this scan, it was discovered that hyperintense petrous bone had been included in the tumor segmentation. CONCLUSIONS We show that an automated segmentation model developed using a restrictive set of siloed institutional data can be successfully adapted for data from different imaging systems and patient populations. This is an important step toward the validation of automated VS segmentation. However, there are significant shortcomings that likely reflect limitations of the data used to train the model. Further validation is needed to make automated segmentation for VS generalizable.
Collapse
Affiliation(s)
- Krish Suresh
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts
| | - Guibo Luo
- Department of Radiology, Massachusetts General Hospital, Harvard University, Boston, Massachusetts
| | - Ryan A Bartholomew
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts
| | - Alyssa Brown
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts
| | - Amy F Juliano
- Department of Radiology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts
| | - Daniel J Lee
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts
| | - D Bradley Welling
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts
| | - Wenli Cai
- Department of Radiology, Massachusetts General Hospital, Harvard University, Boston, Massachusetts
| | - Matthew G Crowson
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts
| |
Collapse
|
5
|
Namestnikova DD, Cherkashova EA, Gumin IS, Chekhonin VP, Yarygin KN, Gubskiy IL. Estimation of the Ischemic Lesion in the Experimental Stroke Studies Using Magnetic Resonance Imaging (Review). Bull Exp Biol Med 2024; 176:649-657. [PMID: 38733482 DOI: 10.1007/s10517-024-06086-z] [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: 10/27/2023] [Indexed: 05/13/2024]
Abstract
In translational animal study aimed at evaluation of the effectiveness of innovative methods for treating cerebral stroke, including regenerative cell technologies, of particular importance is evaluation of the dynamics of changes in the volume of the cerebral infarction in response to therapy. Among the methods for assessing the focus of infarction, MRI is the most effective and convenient tool for use in preclinical studies. This review provides a description of MR pulse sequences used to visualize cerebral ischemia at various stages of its development, and a detailed description of the MR semiotics of cerebral infarction. A comparison of various methods for morphometric analysis of the focus of a cerebral infarction, including systems based on artificial intelligence for a more objective measurement of the volume of the lesion, is also presented.
Collapse
Affiliation(s)
- D D Namestnikova
- Federal Center of Brain Research and Neurotechnologies, Federal Medical-Biological Agency of Russia, Moscow, Russia
- Pirogov Russian National Research Medical University, Ministry of Health of the Russian Federation, Moscow, Russia
| | - E A Cherkashova
- Federal Center of Brain Research and Neurotechnologies, Federal Medical-Biological Agency of Russia, Moscow, Russia
- Pirogov Russian National Research Medical University, Ministry of Health of the Russian Federation, Moscow, Russia
| | - I S Gumin
- Federal Center of Brain Research and Neurotechnologies, Federal Medical-Biological Agency of Russia, Moscow, Russia
| | - V P Chekhonin
- Pirogov Russian National Research Medical University, Ministry of Health of the Russian Federation, Moscow, Russia
- V. P. Serbsky National Medical Research Center of Psychiatry and Narcology, Ministry of Health of the Russian Federation, Moscow, Russia
| | - K N Yarygin
- V. N. Orekhovich Research Institute of Biomedical Chemistry, Moscow, Russia
- Russian Medical Academy of Continuous Professional Education, Ministry of Health of the Russian Federation, Moscow, Russia
| | - I L Gubskiy
- Federal Center of Brain Research and Neurotechnologies, Federal Medical-Biological Agency of Russia, Moscow, Russia.
- Pirogov Russian National Research Medical University, Ministry of Health of the Russian Federation, Moscow, Russia.
| |
Collapse
|
6
|
Requist MR, Mills MK, Carroll KL, Lenz AL. Quantitative Skeletal Imaging and Image-Based Modeling in Pediatric Orthopaedics. Curr Osteoporos Rep 2024; 22:44-55. [PMID: 38243151 DOI: 10.1007/s11914-023-00845-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/19/2023] [Indexed: 01/21/2024]
Abstract
PURPOSE OF REVIEW Musculoskeletal imaging serves a critical role in clinical care and orthopaedic research. Image-based modeling is also gaining traction as a useful tool in understanding skeletal morphology and mechanics. However, there are fewer studies on advanced imaging and modeling in pediatric populations. The purpose of this review is to provide an overview of recent literature on skeletal imaging modalities and modeling techniques with a special emphasis on current and future uses in pediatric research and clinical care. RECENT FINDINGS While many principles of imaging and 3D modeling are relevant across the lifespan, there are special considerations for pediatric musculoskeletal imaging and fewer studies of 3D skeletal modeling in pediatric populations. Improved understanding of bone morphology and growth during childhood in healthy and pathologic patients may provide new insight into the pathophysiology of pediatric-onset skeletal diseases and the biomechanics of bone development. Clinical translation of 3D modeling tools developed in orthopaedic research is limited by the requirement for manual image segmentation and the resources needed for segmentation, modeling, and analysis. This paper highlights the current and future uses of common musculoskeletal imaging modalities and 3D modeling techniques in pediatric orthopaedic clinical care and research.
Collapse
Affiliation(s)
- Melissa R Requist
- Department of Orthopaedics, University of Utah, 590 Wakara Way, Salt Lake City, UT, 84108, USA
- Department of Biomedical Engineering, University of Utah, 36 S Wasatch Dr., Salt Lake City, UT, 84112, USA
| | - Megan K Mills
- Department of Radiology and Imaging Sciences, University of Utah, 30 N Mario Capecchi Dr. 2 South, Salt Lake City, UT, 84112, USA
| | - Kristen L Carroll
- Department of Orthopaedics, University of Utah, 590 Wakara Way, Salt Lake City, UT, 84108, USA
- Shriners Hospital for Children, 1275 E Fairfax Rd, Salt Lake City, UT, 84103, USA
| | - Amy L Lenz
- Department of Orthopaedics, University of Utah, 590 Wakara Way, Salt Lake City, UT, 84108, USA.
- Department of Biomedical Engineering, University of Utah, 36 S Wasatch Dr., Salt Lake City, UT, 84112, USA.
| |
Collapse
|
7
|
Zhou N, Zhu H, Jiang P, Hu Q, Feng Y, Chen W, Zhou K, Hu Y, Zhou Z. Quantification of Endometrial Fibrosis Using Noninvasive MRI T2 Mapping: Initial Findings. J Magn Reson Imaging 2023; 58:1703-1713. [PMID: 37074789 DOI: 10.1002/jmri.28746] [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: 08/19/2022] [Revised: 04/02/2023] [Accepted: 04/03/2023] [Indexed: 04/20/2023] Open
Abstract
BACKGROUND Endometrial fibrosis may cause infertility. Accurate evaluation of endometrial fibrosis helps clinicians to schedule timely therapy. PURPOSE To explore T2 mapping for assessing endometrial fibrosis. STUDY TYPE Prospective. POPULATION Ninety-seven women with severe endometrial fibrosis (SEF) and 21 patients with mild to moderate endometrial fibrosis (MMEF), diagnosed by hysteroscopy, and 37 healthy women. FIELD STRENGTH/SEQUENCE 3T, T2-weighted turbo spin echo (T2-weighted imaging) and multi-echo turbo spin echo (T2 mapping) sequences. ASSESSMENT Endometrial MRI parameters (T2, thickness [ET], area [EA], and volume [EV]) were measured by N.Z. and Q.H. (9- and 4-years' experience in pelvic MRI) and compared between the three subgroups. A multivariable model including MRI parameters and clinical variables (including age and body mass index [BMI]) was developed to predict endometrial fibrosis assessed by hysteroscopy. STATISTICAL TESTS Kruskal-Wallis; ANOVA; Spearman's correlation coefficient (rho); area under the receiver operating characteristic curve (AUC); binary logistic regression; intraclass correlation coefficient (ICC). P value <0.05 for statistical significance. RESULTS Endometrial T2, ET, EA, and EV of MMEF patients (185 msec, 8.2 mm, 168 mm2 , and 2181 mm3 ) and SEF patients (164 msec, 6.7 mm, 120 mm2 , and 1762 mm3 ) were significantly lower than those of healthy women (222 msec, 11.7 mm, 316 mm2 , and 3960 mm3 ). Endometrial T2 and ET of SEF patients were significantly lower than those of MMEF patients. Endometrial T2, ET, EA, and EV were significantly correlated to the degree of endometrial fibrosis (rho = -0.623, -0.695, -0.694, -0.595). There were significant strong correlations between ET, EA, and EV in healthy women and MMEF patients (rho = 0.850-0.908). Endometrial MRI parameters and the multivariable model accurately distinguished MMEF or SEF from normal endometrium (AUCs >0.800). Age, BMI, and MRI parameters in univariable analysis and age and T2 in multivariable analysis significantly predicted endometrial fibrosis. The reproducibility of MRI parameters was excellent (ICC, 0.859-0.980). DATA CONCLUSION T2 mapping has potential to noninvasively and quantitatively evaluate the degree of endometrial fibrosis. EVIDENCE LEVEL 2 Technical Efficacy: Stage 2.
Collapse
Affiliation(s)
- Nan Zhou
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - Hui Zhu
- Department of Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - Peipei Jiang
- Department of Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - Qing Hu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - Yongjing Feng
- Department of Radiology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, China
| | | | - Kefeng Zhou
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - Yali Hu
- Department of Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - Zhengyang Zhou
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| |
Collapse
|
8
|
Wang J, Peng Y, Jing S, Han L, Li T, Luo J. A deep-learning approach for segmentation of liver tumors in magnetic resonance imaging using UNet+. BMC Cancer 2023; 23:1060. [PMID: 37923988 PMCID: PMC10623778 DOI: 10.1186/s12885-023-11432-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: 02/28/2023] [Accepted: 09/21/2023] [Indexed: 11/06/2023] Open
Abstract
OBJECTIVE Radiomic and deep learning studies based on magnetic resonance imaging (MRI) of liver tumor are gradually increasing. Manual segmentation of normal hepatic tissue and tumor exhibits limitations. METHODS 105 patients diagnosed with hepatocellular carcinoma were retrospectively studied between Jan 2015 and Dec 2020. The patients were divided into three sets: training (n = 83), validation (n = 11), and internal testing (n = 11). Additionally, 9 cases were included from the Cancer Imaging Archive as the external test set. Using the arterial phase and T2WI sequences, expert radiologists manually delineated all images. Using deep learning, liver tumors and liver segments were automatically segmented. A preliminary liver segmentation was performed using the UNet + + network, and the segmented liver mask was re-input as the input end into the UNet + + network to segment liver tumors. The false positivity rate was reduced using a threshold value in the liver tumor segmentation. To evaluate the segmentation results, we calculated the Dice similarity coefficient (DSC), average false positivity rate (AFPR), and delineation time. RESULTS The average DSC of the liver in the validation and internal testing sets was 0.91 and 0.92, respectively. In the validation set, manual and automatic delineation took 182.9 and 2.2 s, respectively. On an average, manual and automatic delineation took 169.8 and 1.7 s, respectively. The average DSC of liver tumors was 0.612 and 0.687 in the validation and internal testing sets, respectively. The average time for manual and automatic delineation and AFPR in the internal testing set were 47.4 s, 2.9 s, and 1.4, respectively, and those in the external test set were 29.5 s, 4.2 s, and 1.6, respectively. CONCLUSION UNet + + can automatically segment normal hepatic tissue and liver tumors based on MR images. It provides a methodological basis for the automated segmentation of liver tumors, improves the delineation efficiency, and meets the requirement of extraction set analysis of further radiomics and deep learning.
Collapse
Affiliation(s)
- Jing Wang
- Department of General medicine, The First Medical Center Department of Chinese PLA General Hospital, Peking, 100039, China
| | - Yanyang Peng
- Department of Radiology, First Medical Center of General Hospital of People's Liberation Army, Peking, China
| | - Shi Jing
- Department of Oncology, Huaihe Hospital, Henan University, Kaifeng, 475000, China
| | - Lujun Han
- Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Cancer for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510030, China.
- Translational Medical Center of Huaihe Hospital, Henan University, 115 West Gate Street, Kaifeng, 475000, China.
| | - Tian Li
- School of Basic Medicine, Fourth Military Medical University, Xi'an, 710032, China.
- Translational Medical Center of Huaihe Hospital, Henan University, 115 West Gate Street, Kaifeng, 475000, China.
| | - Junpeng Luo
- Translational Medical Center of Huaihe Hospital, Henan University, 115 West Gate Street, Kaifeng, 475000, China.
- Academy for Advanced Interdisciplinary Studies, Henan University, Zhengzhou, 450046, China.
| |
Collapse
|
9
|
Agadi K, Dominari A, Tebha SS, Mohammadi A, Zahid S. Neurosurgical Management of Cerebrospinal Tumors in the Era of Artificial Intelligence : A Scoping Review. J Korean Neurosurg Soc 2023; 66:632-641. [PMID: 35831137 PMCID: PMC10641423 DOI: 10.3340/jkns.2021.0213] [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: 08/23/2021] [Revised: 10/06/2021] [Accepted: 03/14/2022] [Indexed: 11/27/2022] Open
Abstract
Central nervous system tumors are identified as tumors of the brain and spinal cord. The associated morbidity and mortality of cerebrospinal tumors are disproportionately high compared to other malignancies. While minimally invasive techniques have initiated a revolution in neurosurgery, artificial intelligence (AI) is expediting it. Our study aims to analyze AI's role in the neurosurgical management of cerebrospinal tumors. We conducted a scoping review using the Arksey and O'Malley framework. Upon screening, data extraction and analysis were focused on exploring all potential implications of AI, classification of these implications in the management of cerebrospinal tumors. AI has enhanced the precision of diagnosis of these tumors, enables surgeons to excise the tumor margins completely, thereby reducing the risk of recurrence, and helps to make a more accurate prediction of the patient's prognosis than the conventional methods. AI also offers real-time training to neurosurgeons using virtual and 3D simulation, thereby increasing their confidence and skills during procedures. In addition, robotics is integrated into neurosurgery and identified to increase patient outcomes by making surgery less invasive. AI, including machine learning, is rigorously considered for its applications in the neurosurgical management of cerebrospinal tumors. This field requires further research focused on areas clinically essential in improving the outcome that is also economically feasible for clinical use. The authors suggest that data analysts and neurosurgeons collaborate to explore the full potential of AI.
Collapse
Affiliation(s)
- Kuchalambal Agadi
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
| | - Asimina Dominari
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
- Aristotle University of Thessaloniki School of Medicine, Thessaloniki, Greece
| | - Sameer Saleem Tebha
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
- Department of Neurosurgery and Neurology, Jinnah Medical and Dental College, Karachi, Pakistan
| | - Asma Mohammadi
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
| | - Samina Zahid
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
| |
Collapse
|
10
|
Wang K, George-Jones NA, Chen L, Hunter JB, Wang J. Joint Vestibular Schwannoma Enlargement Prediction and Segmentation Using a Deep Multi-task Model. Laryngoscope 2023; 133:2754-2760. [PMID: 36495306 PMCID: PMC10256836 DOI: 10.1002/lary.30516] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 11/17/2022] [Accepted: 11/20/2022] [Indexed: 12/14/2022]
Abstract
OBJECTIVE To develop a deep-learning-based multi-task (DMT) model for joint tumor enlargement prediction (TEP) and automatic tumor segmentation (TS) for vestibular schwannoma (VS) patients using their initial diagnostic contrast-enhanced T1-weighted (ceT1) magnetic resonance images (MRIs). METHODS Initial ceT1 MRIs for VS patients meeting the inclusion/exclusion criteria of this study were retrospectively collected. VSs on the initial MRIs and their first follow-up scans were manually contoured. Tumor volume and enlargement ratio were measured based on expert contours. A DMT model was constructed for jointly TS and TEP. The manually segmented VS volume on the initial scan and the tumor enlargement label (≥20% volumetric growth) were used as the ground truth for training and evaluating the TS and TEP modules, respectively. RESULTS We performed 5-fold cross-validation with the eligible patients (n = 103). Median segmentation dice coefficient, prediction sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were measured and achieved the following values: 84.20%, 0.68, 0.78, 0.72, and 0.77, respectively. The segmentation result is significantly better than the separate TS network (dice coefficient of 83.13%, p = 0.03) and marginally lower than the state-of-the-art segmentation model nnU-Net (dice coefficient of 86.45%, p = 0.16). The TEP performance is significantly better than the single-task prediction model (AUC = 0.60, p = 0.01) and marginally better than a radiomics-based prediction model (AUC = 0.70, p = 0.17). CONCLUSION The proposed DMT model is of higher learning efficiency and achieves promising performance on TEP and TS. The proposed technology has the potential to improve VS patient management. LEVEL OF EVIDENCE NA Laryngoscope, 133:2754-2760, 2023.
Collapse
Affiliation(s)
- Kai Wang
- The Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Nicholas A George-Jones
- The Department of Otolaryngology-Head and Neck Surgery, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- The Department of Otolaryngology-Head and Neck Surgery, University of Iowa Hospitals and Clinics, Iowa City, Iowa, USA
| | - Liyuan Chen
- The Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Jacob B Hunter
- The Department of Otolaryngology-Head and Neck Surgery, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Jing Wang
- The Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| |
Collapse
|
11
|
Mufti N, Chappell J, O'Brien P, Attilakos G, Irzan H, Sokolska M, Narayanan P, Gaunt T, Humphries PD, Patel P, Whitby E, Jauniaux E, Hutchinson JC, Sebire NJ, Atkinson D, Kendall G, Ourselin S, Vercauteren T, David AL, Melbourne A. Use of super resolution reconstruction MRI for surgical planning in Placenta accreta spectrum disorder: Case series. Placenta 2023; 142:36-45. [PMID: 37634372 PMCID: PMC10937261 DOI: 10.1016/j.placenta.2023.08.066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 07/23/2023] [Accepted: 08/17/2023] [Indexed: 08/29/2023]
Abstract
INTRODUCTION Comprehensive imaging using ultrasound and MRI of placenta accreta spectrum (PAS) aims to prevent catastrophic haemorrhage and maternal death. Standard MRI of the placenta is limited by between-slice motion which can be mitigated by super-resolution reconstruction (SRR) MRI. We applied SRR in suspected PAS cases to determine its ability to enhance anatomical placental assessment and predict adverse maternal outcome. METHODS Suspected PAS patients (n = 22) underwent MRI at a gestational age (weeks + days) of (32+3±3+2, range (27+1-38+6)). SRR of the placental-myometrial-bladder interface involving rigid motion correction of acquired MRI slices combined with robust outlier detection to reconstruct an isotropic high-resolution volume, was achieved in twelve. 2D MRI or SRR images alone, and paired data were assessed by four radiologists in three review rounds. All radiologists were blinded to results of the ultrasound, original MR image reports, case outcomes, and PAS diagnosis. A Random Forest Classification model was used to highlight the most predictive pathological MRI markers for major obstetric haemorrhage (MOH), bladder adherence (BA), and placental attachment depth (PAD). RESULTS At delivery, four patients had placenta praevia with no abnormal attachment, two were clinically diagnosed with PAS, and six had histopathological PAS confirmation. Pathological MRI markers (T2-dark intraplacental bands, and loss of retroplacental T2-hypointense line) predicting MOH were more visible using SRR imaging (accuracy 0.73), in comparison to 2D MRI or paired imaging. Bladder wall interruption, predicting BA, was only easily detected by paired imaging (accuracy 0.72). Better detection of certain pathological markers predicting PAD was found using 2D MRI (placental bulge and myometrial thinning (accuracy 0.81)), and SRR (loss of retroplacental T2-hypointense line (accuracy 0.82)). DISCUSSION The addition of SRR to 2D MRI potentially improved anatomical assessment of certain pathological MRI markers of abnormal placentation that predict maternal morbidity which may benefit surgical planning.
Collapse
Affiliation(s)
- Nada Mufti
- Elizabeth Garret Anderson Institute for Women's Health, University College London, UK; School of Biomedical Engineering and Imaging Sciences (BMEIS), King's College London, UK.
| | - Joanna Chappell
- School of Biomedical Engineering and Imaging Sciences (BMEIS), King's College London, UK
| | | | | | - Hassna Irzan
- School of Biomedical Engineering and Imaging Sciences (BMEIS), King's College London, UK
| | - Magda Sokolska
- Department of Medical Physics and Biomedical Engineering, University College London Hospitals, UK
| | | | - Trevor Gaunt
- University College London Hospital NHS Foundation Trust, UK
| | | | | | | | - Eric Jauniaux
- Elizabeth Garret Anderson Institute for Women's Health, University College London, UK; University College London Hospital NHS Foundation Trust, UK
| | | | | | - David Atkinson
- Centre for Medical Imaging, University College London, UK
| | - Giles Kendall
- Elizabeth Garret Anderson Institute for Women's Health, University College London, UK; University College London Hospital NHS Foundation Trust, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences (BMEIS), King's College London, UK
| | - Tom Vercauteren
- School of Biomedical Engineering and Imaging Sciences (BMEIS), King's College London, UK
| | - Anna L David
- Elizabeth Garret Anderson Institute for Women's Health, University College London, UK; University College London Hospital NHS Foundation Trust, UK; NIHR, University College London Hospitals BRC, UK
| | - Andrew Melbourne
- School of Biomedical Engineering and Imaging Sciences (BMEIS), King's College London, UK
| |
Collapse
|
12
|
Zhao JY, Cao Q, Chen J, Chen W, Du SY, Yu J, Zeng YM, Wang SM, Peng JY, You C, Xu JG, Wang XY. Development and validation of a fully automatic tissue delineation model for brain metastasis using a deep neural network. Quant Imaging Med Surg 2023; 13:6724-6734. [PMID: 37869331 PMCID: PMC10585546 DOI: 10.21037/qims-22-1216] [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: 11/04/2022] [Accepted: 08/04/2023] [Indexed: 10/24/2023]
Abstract
Background Stereotactic radiosurgery (SRS) treatment planning requires accurate delineation of brain metastases, a task that can be tedious and time-consuming. Although studies have explored the use of convolutional neural networks (CNNs) in magnetic resonance imaging (MRI) for automatic brain metastases delineation, none of these studies have performed clinical evaluation, raising concerns about clinical applicability. This study aimed to develop an artificial intelligence (AI) tool for the automatic delineation of single brain metastasis that could be integrated into clinical practice. Methods Data from 426 patients with postcontrast T1-weighted MRIs who underwent SRS between March 2007 and August 2019 were retrospectively collected and divided into training, validation, and testing cohorts of 299, 42, and 85 patients, respectively. Two Gamma Knife (GK) surgeons contoured the brain metastases as the ground truth. A novel 2.5D CNN network was developed for single brain metastasis delineation. The mean Dice similarity coefficient (DSC) and average surface distance (ASD) were used to assess the performance of this method. Results The mean DSC and ASD values were 88.34%±5.00% and 0.35±0.21 mm, respectively, for the contours generated with the AI tool based on the testing set. The DSC measure of the AI tool's performance was dependent on metastatic shape, reinforcement shape, and the existence of peritumoral edema (all P values <0.05). The clinical experts' subjective assessments showed that 415 out of 572 slices (72.6%) in the testing cohort were acceptable for clinical usage without revision. The average time spent editing an AI-generated contour compared with time spent with manual contouring was 74 vs. 196 seconds, respectively (P<0.01). Conclusions The contours delineated with the AI tool for single brain metastasis were in close agreement with the ground truth. The developed AI tool can effectively reduce contouring time and aid in GK treatment planning of single brain metastasis in clinical practice.
Collapse
Affiliation(s)
- Jie-Yi Zhao
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Qi Cao
- Department of Reproductive Medical Center, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Jing Chen
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Wei Chen
- Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Si-Yu Du
- West China School of Medicine, Sichuan University, Chengdu, China
| | - Jie Yu
- West China School of Public Health, Sichuan University, Chengdu, China
| | - Yi-Miao Zeng
- West China School of Medicine, Sichuan University, Chengdu, China
| | - Shu-Min Wang
- West China School of Medicine, Sichuan University, Chengdu, China
| | - Jing-Yu Peng
- West China School of Medicine, Sichuan University, Chengdu, China
| | - Chao You
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Jian-Guo Xu
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Xiao-Yu Wang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| |
Collapse
|
13
|
Balossier A, Delsanti C, Troude L, Thomassin JM, Roche PH, Régis J. Assessing Tumor Volume for Sporadic Vestibular Schwannomas: A Comparison of Methods of Volumetry. Stereotact Funct Neurosurg 2023; 101:265-276. [PMID: 37531945 DOI: 10.1159/000531337] [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: 12/16/2022] [Accepted: 05/16/2023] [Indexed: 08/04/2023]
Abstract
INTRODUCTION The size of vestibular schwannomas (VS) is a major factor guiding the initial decision of treatment and the definition of tumor control or failure. Accurate measurement and standardized definition are mandatory; yet no standard exist. Various approximation methods using linear measures or segmental volumetry have been reported. We reviewed different methods of volumetry and evaluated their correlation and agreement using our own historical cohort. METHODS We selected patients treated for sporadic VS by Gammaknife radiosurgery (GKRS) in our department. Using the stereotactic 3D T1 enhancing MRI on the day of GKRS, 4 methods of volumetry using linear measurements (5-axis, 3-axis, 3-axis-averaged, and 1-axis) and segmental volumetry were compared to each other. The degree of correlation was evaluated using an intraclass correlation test (ICC 3,1). The agreement between the different methods was evaluated using Bland-Altman diagrams. RESULTS A total of 2,188 patients were included. We observed an excellent ICC between 5-axis volumetry (0.98), 3-axis volumetry (0.96), and 3-axis-averaged volumetry (0.96) and segmental volumetry, respectively, irrespective of the Koos grade or Ohata classification. The ICC for 1-axis volumetry was lower (0.72) and varied depending on the Koos and Ohata subgroups. None of these methods were substitutable. CONCLUSION Although segmental volumetry is deemed the most accurate method, it takes more effort and requires sophisticated computation systems compared to methods of volumetry using linear measurements. 5-axis volumetry affords the best adequacy with segmental volumetry among all methods under assessment, irrespective of the shape of the tumor. 1-axis volumetry should not be used.
Collapse
Affiliation(s)
- Anne Balossier
- Functional and Stereotactic Neurosurgery, AP-HM, Timone Hospital, Marseille, France
- INSERM, INS, Inst Neurosci Syst, Aix Marseille University, Marseille, France
| | - Christine Delsanti
- Functional and Stereotactic Neurosurgery, AP-HM, Timone Hospital, Marseille, France
| | - Lucas Troude
- Department of Neurosurgery, AP-HM, North University Hospital, Marseille, France
| | - Jean-Marc Thomassin
- Department of Head and Neck Surgery, AP-HM, Timone Hospital, Marseille, France
| | - Pierre-Hugues Roche
- Department of Neurosurgery, AP-HM, North University Hospital, Marseille, France
| | - Jean Régis
- Functional and Stereotactic Neurosurgery, AP-HM, Timone Hospital, Marseille, France
- INSERM, INS, Inst Neurosci Syst, Aix Marseille University, Marseille, France
| |
Collapse
|
14
|
Street JS, Pandit AS, Toma AK. Predicting vasospasm risk using first presentation aneurysmal subarachnoid hemorrhage volume: A semi-automated CT image segmentation analysis using ITK-SNAP. PLoS One 2023; 18:e0286485. [PMID: 37262041 DOI: 10.1371/journal.pone.0286485] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 05/17/2023] [Indexed: 06/03/2023] Open
Abstract
PURPOSE Cerebral vasospasm following aneurysmal subarachnoid hemorrhage (aSAH) is a significant complication associated with poor neurological outcomes. We present a novel, semi-automated pipeline, implemented in the open-source medical imaging analysis software ITK-SNAP, to segment subarachnoid blood volume from initial CT head (CTH) scans and use this to predict future radiological vasospasm. METHODS 42 patients were admitted between February 2020 and December 2021 to our tertiary neurosciences center, and whose initial referral CTH scan was used for this retrospective cohort study. Blood load was segmented using a semi-automated random forest classifier and active contour evolution implemented in ITK-SNAP. Clinical data were extracted from electronic healthcare records in order to fit models aimed at predicting radiological vasospasm risk. RESULTS Semi-automated segmentations demonstrated excellent agreement with manual, expert-derived volumes (mean Dice coefficient = 0.92). Total normalized blood volume, extracted from CTH images at first presentation, was significantly associated with greater odds of later radiological vasospasm, increasing by approximately 7% for each additional cm3 of blood (OR = 1.069, 95% CI: 1.021-1.120; p < .005). Greater blood volume was also significantly associated with vasospasm of a higher Lindegaard ratio, of longer duration, and a greater number of discrete episodes. Total blood volume predicted radiological vasospasm with a greater accuracy as compared to the modified Fisher scale (AUC = 0.86 vs 0.70), and was of independent predictive value. CONCLUSION Semi-automated methods provide a plausible pipeline for the segmentation of blood from CT head images in aSAH, and total blood volume is a robust, extendable predictor of radiological vasospasm, outperforming the modified Fisher scale. Greater subarachnoid blood volume significantly increases the odds of subsequent vasospasm, its time course and its severity.
Collapse
Affiliation(s)
- James S Street
- Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, London, United Kingdom
| | - Anand S Pandit
- Victor Horsley Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, Queen Square, London, United Kingdom
- High-Dimensional Neurology, Institute of Neurology, University College London, London, United Kingdom
| | - Ahmed K Toma
- Victor Horsley Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, Queen Square, London, United Kingdom
| |
Collapse
|
15
|
Koechli C, Zwahlen DR, Schucht P, Windisch P. Radiomics and machine learning for predicting the consistency of benign tumors of the central nervous system: A systematic review. Eur J Radiol 2023; 164:110866. [PMID: 37207398 DOI: 10.1016/j.ejrad.2023.110866] [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: 03/14/2023] [Revised: 04/28/2023] [Accepted: 05/03/2023] [Indexed: 05/21/2023]
Abstract
PURPOSE Predicting the consistency of benign central nervous system (CNS) tumors prior to surgery helps to improve surgical outcomes. This review summarizes and analyzes the literature on using radiomics and/or machine learning (ML) for consistency prediction. METHOD The Medical Literature Analysis and Retrieval System Online (MEDLINE) database was screened for studies published in English from January 1st 2000. Data was extracted according to the PRISMA guidelines and quality of the studies was assessed in compliance with the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). RESULTS Eight publications were included focusing on pituitary macroadenomas (n = 5), pituitary adenomas (n = 1), and meningiomas (n = 2) using a retrospective (n = 6), prospective (n = 1), and unknown (n = 1) study design with a total of 763 patients for the consistency prediction. The studies reported an area under the curve (AUC) of 0.71-0.99 for their respective best performing model regarding the consistency prediction. Of all studies, four articles validated their models internally whereas none validated their models externally. Two articles stated making data available on request with the remaining publications lacking information with regard to data availability. CONCLUSIONS The research on consistency prediction of CNS tumors is still at an early stage regarding the use of radiomics and different ML techniques. Best-practice procedures regarding radiomics and ML need to be followed more rigorously to facilitate the comparison between publications and, accordingly, the possible implementation into clinical practice in the future.
Collapse
Affiliation(s)
- Carole Koechli
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland; Universitätsklinik für Neurochirurgie, Bern University Hospital, 3010 Bern, Switzerland.
| | - Daniel R Zwahlen
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland
| | - Philippe Schucht
- Universitätsklinik für Neurochirurgie, Bern University Hospital, 3010 Bern, Switzerland
| | - Paul Windisch
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland
| |
Collapse
|
16
|
Burles F, Williams R, Berger L, Pike GB, Lebel C, Iaria G. The Unresolved Methodological Challenge of Detecting Neuroplastic Changes in Astronauts. Life (Basel) 2023; 13:life13020500. [PMID: 36836857 PMCID: PMC9966542 DOI: 10.3390/life13020500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 02/04/2023] [Accepted: 02/07/2023] [Indexed: 02/15/2023] Open
Abstract
After completing a spaceflight, astronauts display a salient upward shift in the position of the brain within the skull, accompanied by a redistribution of cerebrospinal fluid. Magnetic resonance imaging studies have also reported local changes in brain volume following a spaceflight, which have been cautiously interpreted as a neuroplastic response to spaceflight. Here, we provide evidence that the grey matter volume changes seen in astronauts following spaceflight are contaminated by preprocessing errors exacerbated by the upwards shift of the brain within the skull. While it is expected that an astronaut's brain undergoes some neuroplastic adaptations during spaceflight, our findings suggest that the brain volume changes detected using standard processing pipelines for neuroimaging analyses could be contaminated by errors in identifying different tissue types (i.e., tissue segmentation). These errors may undermine the interpretation of such analyses as direct evidence of neuroplastic adaptation, and novel or alternate preprocessing or experimental paradigms are needed in order to resolve this important issue in space health research.
Collapse
Affiliation(s)
- Ford Burles
- Canadian Space Health Research Network, Department of Psychology, Hotchkiss Brain Institute, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 1N4, Canada
- Correspondence:
| | - Rebecca Williams
- Faculty of Health, School of Human Services, Charles Darwin University, Darwin, NT 0810, Australia
| | - Lila Berger
- Canadian Space Health Research Network, Department of Psychology, Hotchkiss Brain Institute, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - G. Bruce Pike
- Department of Radiology, Department of Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Catherine Lebel
- Department of Radiology, Alberta Children’s Hospital Research Institute, Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Giuseppe Iaria
- Canadian Space Health Research Network, Department of Psychology, Hotchkiss Brain Institute, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 1N4, Canada
| |
Collapse
|
17
|
Jayasinghe HEH, Rathnayake ASS, Wickramasinghe WMIS, Pallewatte AS. Age and sex related variations of adult human ocular volumes in the Sri Lankan population: an evaluation using magnetic resonance imaging. Radiography (Lond) 2023; 29:62-69. [PMID: 36327516 DOI: 10.1016/j.radi.2022.09.012] [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: 05/10/2022] [Revised: 09/26/2022] [Accepted: 09/29/2022] [Indexed: 11/07/2022]
Abstract
INTRODUCTION Variations in the human ocular volumes are related to ocular pathologies including congenital glaucoma, microphthalmus, buphthalmus, and macrophthalmus. As the currently published reference ocular volumes are prone to physiological and racial variations, population specific values may provide more precision in ophthalmological interventions. This study was conducted to assess the age and sex dependent differences in ocular volumes in Sri Lankan individuals using magnetic resonance imaging (MRI). METHODS The study was undertaken using the brain MRI scans from 200 patients which were reported as normal. Study sample consisted of patients between 18 years and 90 years of age with 91 male subjects and 109 female subjects. Two independent observers measured ocular volumes using a software-based method and an MRI planimetry based method. Age and sex of the study participants were recorded for the further analysis. RESULTS Statistically significant differences in both ocular volumes were found between males and females (p < 0.05) when using both volume analysis methods. The mean ± SD ocular volumes obtained as right software based volume, right MRI planimetry volume, left software based volume and left MRI planimetry volume were 6.8 ± 0.6, 6.0 ± 0.6, 6.6 ± 0.7 and 5.9 ± 0.6 cm3 in females and 6.9 ± 0.8, 6.3 ± 0.7, 6.9 ± 0.8 and 6.2 ± 0.7 cm3 in males. While software-based measurements show a significant linear correlation with age in both eyeball volumes, MRI planimetry measurement showed a significant linear correlation with age only in the left eyeball (p < 0.05). Weak negative correlations were found with age in right ocular volume in both MRI planimetry based (r = -0.121) and software based (r = -0.168) measurements and in left ocular volume in MRI planimetry based (r = -0.151) and software based (r = -0.179) measurements. Furthermore, ocular volumes obtained from the software-based method were significantly greater than the MRI planimetry based ocular volumes (p < 0.05) in both eyes, despite having a strong positive correlation. CONCLUSION The mean ocular volumes obtained from this study revealed a significant variation between the right and left eyes as well as a sexual dimorphism. Moreover, since the two measurement methods show a significant difference, the choice of measurement method should depend on the required accuracy of the eye volume decided with respect to the clinical implication. IMPLICATIONS FOR PRACTICE Since there are no reference values for Sri Lankan adult ocular volumes, this study may serve that purpose in the current population, while supporting ophthalmologists and radiologists to quantitatively evaluate ocular pathologies and to follow precise interventions.
Collapse
Affiliation(s)
- H E H Jayasinghe
- Department of Radiography and Radiotherapy, Faculty of Allied Health Sciences, General Sir John Kotelawala Defence University, Werahera, Colombo, Sri Lanka
| | - A S S Rathnayake
- Department of Radiography and Radiotherapy, Faculty of Allied Health Sciences, General Sir John Kotelawala Defence University, Werahera, Colombo, Sri Lanka
| | - W M I S Wickramasinghe
- Department of Radiography and Radiotherapy, Faculty of Allied Health Sciences, General Sir John Kotelawala Defence University, Werahera, Colombo, Sri Lanka
| | - A S Pallewatte
- Neurosurgical Unit, National Hospital, Colombo, Sri Lanka
| |
Collapse
|
18
|
Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review. Diagnostics (Basel) 2022; 12:diagnostics12102535. [DOI: 10.3390/diagnostics12102535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/07/2022] [Accepted: 10/14/2022] [Indexed: 11/16/2022] Open
Abstract
Magnetic resonance imaging (MRI) is a standard tool for the diagnosis of stroke, but its manual interpretation by experts is arduous and time-consuming. Thus, there is a need for computer-aided-diagnosis (CAD) models for the automatic segmentation and classification of stroke on brain MRI. The heterogeneity of stroke pathogenesis, morphology, image acquisition modalities, sequences, and intralesional tissue signal intensity, as well as lesion-to-normal tissue contrast, pose significant challenges to the development of such systems. Machine learning (ML) is increasingly being used in predictive neuroimaging diagnosis and prognostication. This paper reviews image processing and machine learning techniques that have been applied to detect ischemic stroke on brain MRI, including details on image acquisition, pre-processing, techniques to segment, extraction of features, and classification into stroke types. The main objective of this work is to find the state-of-art machine learning techniques used to predict the ischemic stroke and their application in clinical set-up. The article selection is performed according to PRISMA guideline. The state-of-the-art on automated MRI stroke diagnosis, with a focus on machine learning, is discussed, along with its advantages and limitations. We found that the various machine learning models discussed in this article are able to detect the infarcts with an acceptable accuracy of 70–90%. However, no one has highlighted the time complexity to predict the stroke in the model developed, which is an important factor. The work concludes with proposals for future recommendations for building efficient and robust deep learning (DL) models for quantitative brain MRI analysis. In recent work, with the application of DL approaches, using large datasets to train the models has improved the detection accuracy and reduced computational complexity. We suggest that the design of a decision support system based on artificial intelligence (AI) and clinical data presenting symptoms is essential to support clinicians to accelerate diagnosis and timeous therapy in the emergency management of stroke.
Collapse
|
19
|
Machine Learning in the Management of Lateral Skull Base Tumors: A Systematic Review. JOURNAL OF OTORHINOLARYNGOLOGY, HEARING AND BALANCE MEDICINE 2022. [DOI: 10.3390/ohbm3040007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The application of machine learning (ML) techniques to otolaryngology remains a topic of interest and prevalence in the literature, though no previous articles have summarized the current state of ML application to management and the diagnosis of lateral skull base (LSB) tumors. Subsequently, we present a systematic overview of previous applications of ML techniques to the management of LSB tumors. Independent searches were conducted on PubMed and Web of Science between August 2020 and February 2021 to identify the literature pertaining to the use of ML techniques in LSB tumor surgery written in the English language. All articles were assessed in regard to their application task, ML methodology, and their outcomes. A total of 32 articles were examined. The number of articles involving applications of ML techniques to LSB tumor surgeries has significantly increased since the first article relevant to this field was published in 1994. The most commonly employed ML category was tree-based algorithms. Most articles were included in the category of surgical management (13; 40.6%), followed by those in disease classification (8; 25%). Overall, the application of ML techniques to the management of LSB tumor has evolved rapidly over the past two decades, and the anticipated growth in the future could significantly augment the surgical outcomes and management of LSB tumors.
Collapse
|
20
|
Shetty S, Al-Bayatti S, Alam MK, Al-Rawi NH, Kamath V, Tippu SR, Narasimhan S, Al Kawas S, Elsayed W, Rao K, Castelino R. Analysis of inferior nasal turbinate volume in subjects with nasal septum deviation: a retrospective cone beam tomography study. PeerJ 2022; 10:e14032. [PMID: 36172494 PMCID: PMC9511997 DOI: 10.7717/peerj.14032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 08/17/2022] [Indexed: 01/19/2023] Open
Abstract
Background The association of the linear dimensions of the inferior turbinate hypertrophy with nasal septal deviation has been studied recently. However, the volumetric dimensions provide a more accurate status of the turbinate hypertrophy compared to linear measurements. The aim of this study was to analyze the association of inferior nasal turbinate volume with the degree of nasal septal deviation (NSD). Methods A retrospective evaluation of the cone beam computed tomography (CBCT) scans of 412 patients was carried out to obtain 150 scans which were included in the study. The scans were categorized into three groups. Group 1 comprised of 50 scans of patients with no inferior turbinate hypertrophy (ITH) and no nasal septal deviation. Group 2 comprised of 50 scans of patients with ITH and no NSD; whereas Group 3 included 50 scans of patients with ITH and NSD. The total turbinate volume of inferior turbinates (bilateral) were determined by using Vesalius 3D software (PS-Medtech, Amsterdam, Netherlands). Results The intraclass correlation coefficient (ICC) between the volumetric estimations performed by the two radiologists was 0.82. There were no significant age and gender related changes in the total turbinate volume. Patients in Group 3 had significantly higher (p = 0.001) total turbinate volume compared to Group 2 and Group 1. There was a positive and significant correlation (r = 0.52, p = 0.002) between the degree of septal deviation and total turbinate volume. When the total turbinate volume of the patients with different types of septal deviation was compared in Group 3, a statistically significant difference (p = 0.001) was observed. Regression analysis revealed that the septal deviation angle (SDA) (p = 0.001) had a relationship with total turbinate volume. From the results of the study we can conclude that the total turbinate volume is higher in patients with nasal septal deviation. It can also be concluded that the septal deviation angle has a positive correlation with total turbinate volume. The data obtained from the study can be useful in post-surgical follow up and evaluation of patients with nasal septal deviation and hypertrophied inferior nasal turbinate.
Collapse
Affiliation(s)
- Shishir Shetty
- Department of Oral and Craniofacial Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Saad Al-Bayatti
- Department of Oral and Craniofacial Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | | | - Natheer H. Al-Rawi
- Department of Oral and Craniofacial Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Vinayak Kamath
- Department of Public Health Dentistry, Goa Dental College, Goa, India
| | - Shoaib Rahman Tippu
- Department of Diagnostic and Surgical Dental Sciences, Gulf Medical University, Ajman, United Arab Emirates
| | - Sangeetha Narasimhan
- Department of Oral and Craniofacial Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Sausan Al Kawas
- Department of Oral and Craniofacial Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Walid Elsayed
- College of Dentistry, Gulf Medical University, Ajman, United Arab Emirates
| | - Kumuda Rao
- Department of Oral Medicine and Radiology, Nitte (Deemed to be University), Mangalore, India
| | - Renita Castelino
- Department of Oral Medicine and Radiology, Nitte (Deemed to be University), Mangalore, India
| |
Collapse
|
21
|
Sherwani MK, Marzullo A, De Momi E, Calimeri F. Lesion segmentation in lung CT scans using unsupervised adversarial learning. Med Biol Eng Comput 2022; 60:3203-3215. [PMID: 36125656 PMCID: PMC9486778 DOI: 10.1007/s11517-022-02651-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 07/28/2022] [Indexed: 12/01/2022]
Abstract
Lesion segmentation in medical images is difficult yet crucial for proper diagnosis and treatment. Identifying lesions in medical images is costly and time-consuming and requires highly specialized knowledge. For this reason, supervised and semi-supervised learning techniques have been developed. Nevertheless, the lack of annotated data, which is common in medical imaging, is an issue; in this context, interesting approaches can use unsupervised learning to accurately distinguish between healthy tissues and lesions, training the network without using the annotations. In this work, an unsupervised learning technique is proposed to automatically segment coronavirus disease 2019 (COVID-19) lesions on 2D axial CT lung slices. The proposed approach uses the technique of image translation to generate healthy lung images based on the infected lung image without the need for lesion annotations. Attention masks are used to improve the quality of the segmentation further. Experiments showed the capability of the proposed approaches to segment the lesions, and it outperforms a range of unsupervised lesion detection approaches. The average reported results for the test dataset based on the metrics: Dice Score, Sensitivity, Specificity, Structure Measure, Enhanced-Alignment Measure, and Mean Absolute Error are 0.695, 0.694, 0.961, 0.791, 0.875, and 0.082 respectively. The achieved results are promising compared with the state-of-the-art and could constitute a valuable tool for future developments.
Collapse
Affiliation(s)
- Moiz Khan Sherwani
- Department of Mathematics and Computer Science, University of Calabria, Rende, Italy.
| | - Aldo Marzullo
- Department of Mathematics and Computer Science, University of Calabria, Rende, Italy
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Francesco Calimeri
- Department of Mathematics and Computer Science, University of Calabria, Rende, Italy
| |
Collapse
|
22
|
Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review. Cancers (Basel) 2022; 14:cancers14112676. [PMID: 35681655 PMCID: PMC9179850 DOI: 10.3390/cancers14112676] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/18/2022] [Accepted: 05/26/2022] [Indexed: 11/20/2022] Open
Abstract
Simple Summary Machine learning in radiology of the central nervous system has seen many interesting publications in the past few years. Since the focus has largely been on malignant tumors such as brain metastases and high-grade gliomas, we conducted a systematic review on benign tumors to summarize what has been published and where there might be gaps in the research. We found several studies that report good results, but the descriptions of methodologies could be improved to enable better comparisons and assessment of biases. Abstract Objectives: To summarize the available literature on using machine learning (ML) for the detection and segmentation of benign tumors of the central nervous system (CNS) and to assess the adherence of published ML/diagnostic accuracy studies to best practice. Methods: The MEDLINE database was searched for the use of ML in patients with any benign tumor of the CNS, and the records were screened according to PRISMA guidelines. Results: Eleven retrospective studies focusing on meningioma (n = 4), vestibular schwannoma (n = 4), pituitary adenoma (n = 2) and spinal schwannoma (n = 1) were included. The majority of studies attempted segmentation. Links to repositories containing code were provided in two manuscripts, and no manuscripts shared imaging data. Only one study used an external test set, which raises the question as to whether some of the good performances that have been reported were caused by overfitting and may not generalize to data from other institutions. Conclusions: Using ML for detecting and segmenting benign brain tumors is still in its infancy. Stronger adherence to ML best practices could facilitate easier comparisons between studies and contribute to the development of models that are more likely to one day be used in clinical practice.
Collapse
|
23
|
Mufti N, Ebner M, Patel P, Aertsen M, Gaunt T, Humphries PD, Bredaki FE, Hewitt R, Butler C, Sokolska M, Kendall GS, Atkinson D, Vercauteren T, Ourselin S, Pandya PP, Deprest J, Melbourne A, David AL. Super-resolution Reconstruction MRI Application in Fetal Neck Masses and Congenital High Airway Obstruction Syndrome. OTO Open 2021; 5:2473974X211055372. [PMID: 34723053 PMCID: PMC8549475 DOI: 10.1177/2473974x211055372] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 10/06/2021] [Indexed: 11/21/2022] Open
Abstract
Objective Reliable airway patency diagnosis in fetal tracheolaryngeal obstruction is crucial to select and plan ex utero intrapartum treatment (EXIT) surgery. We compared the clinical utility of magnetic resonance imaging (MRI) super-resolution reconstruction (SRR) of the trachea, which can mitigate unpredictable fetal motion effects, with standard 2-dimensional (2D) MRI for airway patency diagnosis and assessment of fetal neck mass anatomy. Study Design A single-center case series of 7 consecutive singleton pregnancies with complex upper airway obstruction (2013-2019). Setting A tertiary fetal medicine unit performing EXIT surgery. Methods MRI SRR of the trachea was performed involving rigid motion correction of acquired 2D MRI slices combined with robust outlier detection to reconstruct an isotropic high-resolution volume. SRR, 2D MRI, and paired data were blindly assessed by 3 radiologists in 3 experimental rounds. Results Airway patency was correctly diagnosed in 4 of 7 cases (57%) with 2D MRI as compared with 2 of 7 cases (29%) with SRR alone or paired 2D MRI and SRR. Radiologists were more confident (P = .026) in airway patency diagnosis when using 2D MRI than SRR. Anatomic clarity was higher with SRR (P = .027) or paired data (P = .041) in comparison with 2D MRI alone. Radiologists detected further anatomic details by using paired images versus 2D MRI alone (P < .001). Cognitive load, as assessed by the NASA Task Load Index, was increased with paired or SRR data in comparison with 2D MRI. Conclusion The addition of SRR to 2D MRI does not increase fetal airway patency diagnostic accuracy but does provide improved anatomic information, which may benefit surgical planning of EXIT procedures.
Collapse
Affiliation(s)
- Nada Mufti
- Elizabeth Garrett Anderson Institute for Women's Health, University College London, London, UK.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Michael Ebner
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Department of Medical Physics and Biomedical Engineering, University College London Hospitals, London, UK
| | - Premal Patel
- Radiology Department, Great Ormond Street Hospital for Children, London, UK
| | - Michael Aertsen
- Department of Radiology, University Hospitals Katholieke Universiteit, Leuven, Belgium
| | - Trevor Gaunt
- Radiology Department, Great Ormond Street Hospital for Children, London, UK.,Women's Health Division, University College London Hospitals, London, UK
| | - Paul D Humphries
- Radiology Department, Great Ormond Street Hospital for Children, London, UK
| | | | - Richard Hewitt
- Ear, Nose and Throat Department, Great Ormond Street Hospital for Children, London, UK
| | - Colin Butler
- Ear, Nose and Throat Department, Great Ormond Street Hospital for Children, London, UK
| | - Magdalena Sokolska
- Department of Medical Physics and Biomedical Engineering, University College London Hospitals, London, UK
| | - Giles S Kendall
- Elizabeth Garrett Anderson Institute for Women's Health, University College London, London, UK.,Women's Health Division, University College London Hospitals, London, UK
| | - David Atkinson
- Department of Medical Physics and Biomedical Engineering, University College London Hospitals, London, UK.,Centre for Medical Imaging, University College London, London, UK
| | - Tom Vercauteren
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Department of Medical Physics and Biomedical Engineering, University College London Hospitals, London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Department of Medical Physics and Biomedical Engineering, University College London Hospitals, London, UK
| | - Pranav P Pandya
- Women's Health Division, University College London Hospitals, London, UK
| | - Jan Deprest
- Elizabeth Garrett Anderson Institute for Women's Health, University College London, London, UK.,Department of Obstetrics and Gynaecology, University Hospitals Katholieke Universiteit, Leuven, Belgium
| | - Andrew Melbourne
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Department of Medical Physics and Biomedical Engineering, University College London Hospitals, London, UK
| | - Anna L David
- Elizabeth Garrett Anderson Institute for Women's Health, University College London, London, UK.,Department of Obstetrics and Gynaecology, University Hospitals Katholieke Universiteit, Leuven, Belgium
| |
Collapse
|
24
|
Requist MR, Sripanich Y, Peterson AC, Rolvien T, Barg A, Lenz AL. Semi-automatic micro-CT segmentation of the midfoot using calibrated thresholds. Int J Comput Assist Radiol Surg 2021; 16:387-396. [PMID: 33606178 DOI: 10.1007/s11548-021-02318-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 01/19/2021] [Indexed: 10/22/2022]
Abstract
PURPOSE In the field of skeletal research, accurate and reliable segmentation methods are necessary for quantitative micro-CT analysis to assess bone quality. We propose a method of semi-automatic image segmentation of the midfoot, using the cuneiform bones as a model, based on thresholds set by phantom calibration that allows reproducible results in low cortical thickness bones. METHODS Manual and semi-automatic segmentation methods were compared in micro-CT scans of the medial and intermediate cuneiforms of 24 cadaveric specimens. The manual method used intensity thresholds, hole filling, and manual cleanup. The semi-automatic method utilized calibrated bone and soft tissue thresholds Boolean subtraction to cleanly identify edges before hole filling. Intra- and inter-rater reliability was tested for the semi-automatic method in all specimens. Mask volume and average bone mineral density (BMD) were measured for all masks, and the three-dimensional models were compared to the initial semi-automatic segmentation using an unsigned distance part comparison analysis. Segmentation methods were compared with paired t-tests with significance level 0.05, and reliability was analyzed by calculating intra-class correlation coefficients. RESULTS There were statistically significant differences in mask volume and BMD between the manual and semi-automatic segmentation methods in both bones. The intra- and inter-reliability was excellent for mask volume and bone density in both bones. Part comparisons showed a higher maximum distance between surfaces for the manual segmentation than the repeat semi-automatic segmentations. CONCLUSION We developed a semi-automatic micro-CT segmentation method based on calibrated thresholds. This method was designed specifically for use in bones with high rates of curvature and low cortical bone density, such as the cuneiforms, where traditional threshold-based segmentation is more challenging. Our method shows improvement over manual segmentation and was highly reliable, making it appropriate for use in quantitative micro-CT analysis.
Collapse
Affiliation(s)
- Melissa R Requist
- Department of Orthopaedics, University of Utah, 590 Wakara Way, Salt Lake City, UT, 84108, USA.,Department of Biomedical Engineering, University of Arizona, 1127 E James E Rogers Way, Tucson, AZ, 85721, USA
| | - Yantarat Sripanich
- Department of Orthopaedics, University of Utah, 590 Wakara Way, Salt Lake City, UT, 84108, USA.,Department of Orthopaedics, Phramongkutklao Hospital and College of Medicine, 315 Rajavithi Road, Tung Phayathai, Ratchathewi, Bangkok, 10400, Thailand
| | - Andrew C Peterson
- Department of Orthopaedics, University of Utah, 590 Wakara Way, Salt Lake City, UT, 84108, USA
| | - Tim Rolvien
- Department of Trauma and Orthopaedic Surgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Alexej Barg
- Department of Orthopaedics, University of Utah, 590 Wakara Way, Salt Lake City, UT, 84108, USA. .,Department of Trauma and Orthopaedic Surgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany.
| | - Amy L Lenz
- Department of Orthopaedics, University of Utah, 590 Wakara Way, Salt Lake City, UT, 84108, USA.
| |
Collapse
|