Main Page/SlicerCommunity/2022

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The community that relies on 3D Slicer is large and active: (numbers below updated on December 1^st, 2023)

• 1,467,466+ downloads in the last 11 years (269,677 in 2023, 206,541 in 2022)
• over 17.900+ literature search results on Google Scholar
  • 13,400+ cancer
  • 7.290+ brain
  • 6,380+ lung
  • 3,980+ breast
  • 3,080+ prostate

• 2,147+ papers on PubMed citing the Slicer platform paper
  • Fedorov A., Beichel R., Kalpathy-Cramer J., Finet J., Fillion-Robin J-C., Pujol S., Bauer C., Jennings D., Fennessy F.M., Sonka M., Buatti J., Aylward S.R., Miller J.V., Pieper S., Kikinis R. 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network. Magnetic Resonance Imaging. 2012 Nov;30(9):1323-41. PMID: 22770690. PMCID: PMC3466397.

• 39 events in open source hackathon series continuously running since 2005 with 3260 total participants
• Slicer Forum with +8,138 subscribers has approximately 275 posts every week

The following is a sample of the research performed using 3D Slicer outside of the group that develops it. in 2022

We monitor PubMed and related databases to update these lists, but if you know of other research related to the Slicer community that should be included here please email: marianna (at) bwh.harvard.edu.

2022

An Automatic Measurement System of Distal Femur Morphological Parameters Using 3D Slicer Software

Publication: Bone. 2022 Mar;156:116300. PMID: 34958998 | PDF Authors: Chen Z, Wang Y, Li X, Wang K, Li Z, Yang P. Institution: College of Computer Science, Xi'an University of Posts and Telecommunications, Shaanxi, China. Abstract: In the field of joint surgery, the computer-aided design of knee prostheses suitable for the Chinese population requires a large quantity of anatomical knee data. In this study, we propose a new method that uses 3D Slicer software to automatically measure the morphological parameters of the distal femur. First, 141 femur samples were segmented from CT data to establish the femoral shape library. Next, balanced iterative reducing and clustering using hierarchies (BIRCH) combined with iterative closest point (ICP) and generalised procrustes analysis (GPA) were used to achieve fast registration of the femur samples. The statistical model was automatically calculated from the registered femur samples, and an orthopaedic surgeon marked the points on the statistical model. Finally, we developed an automatic measurement system using 3D Slicer software, and a deformable model matching method was applied to establish the point correspondence between the statistical model and the other samples. By matching points on the statistical model to corresponding points in other samples, we measured all other samples. We marked six points and measured eight parameters. We evaluated the performance of automatic matching by comparing the points marked manually with those matched automatically and verified the accuracy of the system by comparing the manual and automatic measurement results. The results indicated that the average error of the automatic matching points was 1.03 mm, and the average length error and average angle error measured automatically by the system were 0.37 mm and 0.63°, respectively. These errors were smaller than the intra-rater and inter-rater errors measured manually by two different surgeons, which showed that the accuracy of our automatic method was high. Taken together, this study established an accurate and automatic measurement system for the distal femur based on the secondary development of 3D Slicer software to assist orthopaedic surgeons in completing the measurements of big data and further promote the improved design of Chinese-specific knee prostheses.

Knee Joint Unloading and Daily Physical Activity Associate With Cartilage T2 Relaxation Times 1 Month After ACL Injury

Publication: J Orthop Res. 2022 Jan;40(1):138-49. PMID: 33783030 | PDF Authors: Wellsandt E, Kallman T, Golightly Y, Podsiadlo D, Dudley A, Vas S, Michaud K, Tao M, Sajja B, Manzer M. Institution: Division of Physical Therapy Education, University of Nebraska Medical Center, Omaha, NE, USA. Abstract: Osteoarthritis (OA) is prevalent after anterior cruciate ligament (ACL) injury, but mechanismsunderlying its development are poorly understood. The purpose of this study was to determine if gait biomechanics and daily physical activity (PA) associate with cartilage T2 relaxation times, a marker of collagen organization and water content, 1 month after ACL injury. Twenty-seven participants (15-35 years old) without chondral lesions completed magnetic resonance imaging, three-dimensional gait analysis, and 1 week of PA accelerometry. Interlimb differences and ratios were calculated for gait biomechanics and T2 relaxation times, respectively. Multiple linear regression models adjusted for age, sex, and concomitant meniscus injury were used to determine the association between gait biomechanics and PA with T2 relaxation times, respectively. Altered knee adduction moment (KAM) impulse, less knee flexion excursion (kEXC) and higher daily step counts accounted for 35.8%-65.8% of T2 relaxation time variation in the weightbearing and posterior cartilage of the medial and lateral compartment (all p ≤ .011). KAM impulse was the strongest factor for T2 relaxation times in all models (all p ≤ .001). Lower KAM impulse associated with longer T2 relaxation times in the injured medial compartment (β = -.720 to -.901) and shorter T2 relaxation in the lateral compartment (β = .713 to .956). At 1 month after ACL injury, altered KAM impulse, less kEXC, and higher PA associated with longer T2 relaxation times, which may indicate poorer cartilage health. Statement of Clinical Significance: Gait biomechanics and daily PA are modifiable targets that may improve cartilage health acutely after ACL injury and slow progression to OA. "Before uninjured segmentation, a manual and affine registration technique was used to register the injured reference images to the uninjured knee using 3D Slicer software."

Knee Cartilage T 2 Relaxation Times 3 Months After ACL Reconstruction Are Associated With Knee Gait Variables Linked to Knee Osteoarthritis

Publication: J Orthop Res. 2022 Jan;40(1):252-9. PMID: 33783867 Authors: Williams JR, Neal K, Alfayyadh A, Lennon K, Capin JJ, Khandha A, Manal K, Potter HG, Snyder-Mackler L, Buchanan TS. Institution: Department of Mechanical Engineering, University of Delaware, Newark, DE, USA. Abstract: Osteoarthritis development after ACL reconstruction (ACLR) is not well understood. Investigators have examined associations between knee biomechanical alterations and quantitative MRI (qMRI) variables, reflective of cartilage health, 12-60 months following ACLR; however, none have done so early after surgery. As part of an exploratory study, 45 individuals (age, 23 ± 7 years) underwent motion analysis during walking and qMRI 3 months after ACLR. For each limb, peak knee adduction moment (pKAM) and peak knee flexion moment (pKFM) were determined using inverse dynamics and peak medial compartment force was calculated using a neuromusculoskeletal model. T2 relaxation times in the medial compartment and linear regressions were used to determine the associations between gait variables and deep and superficial cartilage T2 relaxation times in six regions. pKAM was positively associated with deep layer T2 relaxation times within the femoral central and posterior regions when examined in the involved limb and from an interlimb difference perspective (involved limb - uninvolved limb). After adjusting for age, the association between interlimb difference of pKAM and interlimb difference of deep layer T2 relaxation times in the tibial central region became significant (p = .043). Interlimb difference of pKFM was negatively associated with interlimb difference of deep layer T2 relaxation times within the femoral central and posterior regions. These associations suggest that degenerative pathways leading to osteoarthritis may be detectable as early as 3 months after reconstruction. Preventative therapeutic techniques may need to be employed early in the rehabilitation process to prevent cartilage degradation. "All images were analyzed using the 3D Slicer software."

COVID-19 CT Scan Lung Segmentation: How We Do It

Publication: J Digit Imaging. 2022 Jan 28:1–8. PMID: 35091874 | PDF Authors: Negroni D, Zagaria D, Paladini A, Falaschi Z, Arcoraci A, Barini M, Carriero A. Institution: Department of Radiology, "Maggiore Della Carità" Hospital, Novara, Italy. Abstract: The National Health Systems have been severely stressed out by the COVID-19 pandemic because 14% of patients require hospitalization and oxygen support, and 5% require admission to an Intensive Care Unit (ICU). Relationship between COVID-19 prognosis and the extent of alterations on chest CT obtained by both visual and software-based quantification that expresses objective evaluations of the percentage of ventilated lung parenchyma compared to the affected one has been proven. While commercial applications for automatic medical image computing and visualization are expensive and limited in their spread, the open-source systems are characterized by not enough standardization and time-consuming troubles. We analyzed chest CT exams on 246 patients suspected of COVID-19 performed in the Emergency Department CT room. The lung parenchyma segmentation was obtained by a threshold-based method using the open-source 3D Slicer software and software tools called "Segment Editor" and "Segment Quantification." For the three main characteristics analyzed on lungs affected by COVID-19 pneumonia, a specifical densitometry value range was defined: from - 950 to - 700 HU for well-aerated parenchyma; from - 700 to - 250 HU for interstitial lung disease; from - 250 to 250 HU for parenchymal consolidation. For the well-aerated parenchyma and the interstitial alterations, the procedure was semi-automatic with low time consumption, whereas consolidations' analysis needed manual interventions by the operator. After the chest CT, 13% of the sample was admitted to intensive care, while 34% of them to the sub-intensive care. In patients moved to intensive care, the parenchyma analysis reported a higher crazy paving presentation. The quantitative analysis of the alterations affecting the lung parenchyma of patients with COVID-19 pneumonia can be performed by threshold method segmentation on 3D Slicer. The segmentation could have an important role in the quantification in different COVID-19 pneumonia presentations, allowing to help the clinician in the correct management of patients.

Prognostic Value of Tumor Measurement Parameters and SCC-Ag Changes in Patients With Locally-Advanced Cervical Cancer

Publication: Radiat Oncol. 2022 Jan 10;17(1):6. PMID: 35012582 | PDF Authors: Chen W, Xiu S, Xie X, Guo H, Xu Y, Bai P, Xia X. Institution: Department of Radiation Oncology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fujian, China. Abstract: Objective: To investigate the prognostic relevance of specific measurement parameters such as tumor diameter, tumor volume, tumor volume reduction rate (TVRR), and changes in the squamous cell carcinoma antigen (SCC-Ag) level in patients with locally-advanced cervical cancer (LACC) undergoing concurrent radiotherapy and chemotherapy. Methods: This was a retrospective study of 203 patients with stage IIA-IVA cervical squamous cell carcinoma who were newly diagnosed at our hospital between January 2011 and March 2015. Clinical data and pre-and post-treatment imaging information were collected and each parameter was calculated using 3D Slicer software. The pre/post-treatment tumor diameter (TDpre/post), tumor volume (TVpre/post), SCC-Ag (SCCpre/post), and TVRR, SCC-Ag reduction rate (SCCRR) were analyzed and their prognostic relevance evaluated. Results: The median follow-up was 69 months. The 5-year overall survival (OS) and disease progression-free survival (PFS) rates were 69.5% and 64.5%, respectively. On univariate analysis, TDpre/post, TVpre/post, TVRR, SCCpre/post and SCCRR showed significant association with OS and PFS (P < 0.05). On multivariate analysis, TDpre [Hazard ratio (HR) = 0.373, P = 0.028], TDpost (HR = 0.376, P = 0.003) and SCCpost (HR = 0.374, P = 0.001) were independent predictors of OS. TVRR (HR = 2.998, P < 0.001), SCCpre (HR = 0.563, P = 0.041), and SCCpost (HR = 0.253, P < 0.001) were independent predictors of PFS. Tumor measurement parameters showed a positive correlation with SCC-Ag (P < 0.05). Conclusion: TDpre/post, TVpre/post, TVRR, SCCpre/post, and SCCRR were prognostic factors in LACC. TDpre/post and SCCpost showed the most significant prognostic value. TVRR and SCCpre/post were closely related to disease progression. Further studies should investigate the correlation between measurement parameters of tumor and SCC-Ag.

MRI Radiomic Features-Based Machine Learning Approach to Classify Ischemic Stroke Onset Time

Publication: J Neurol. 2022 Jan;269(1):350-60. PMID: 34218292 Authors: Zhang YQ, Liu AF, Man FY, Zhang YY, Li C, Liu YE, Zhou J, Zhang AP, Zhang YD, Lv J, Jiang WJ. Institution: Department of Vascular Neurosurgery, New Era Stroke Care and Research Institute, The PLA Rocket Force Characteristic Medical Center, Beijing, China. Abstract: Purpose: We aimed to investigate the ability of MRI radiomics features-based machine learning (ML) models to classify the time since stroke onset (TSS), which could aid in stroke assessment and treatment options. Methods: This study involved 84 patients with acute ischemic stroke due to anterior circulation artery occlusion (51 in the training cohort and 33 in the independent test cohort). Region of infarct segmentation was manually outlined by 3D Slicer software. Image processing including registration, normalization and radiomics features calculation were done in R (version 3.6.1). A total of 4312 radiomic features from each image sequence were captured and used in six ML models to estimate stroke onset time for binary classification (≤ 4.5 h). Receiver-operating characteristic curve (ROC) and other parameters were calculated to evaluate the performance of the models in both training and test cohorts. Results: Twelve radiomics and six clinic features were selected to construct the ML models for TSS classification. The deep learning model-based DWI/ADC radiomic features performed the best for binary TSS classification in the independent test cohort, with an AUC of 0.754, accuracy of 0.788, sensitivity of 0.952, specificity of 0.500, positive predictive value of 0.769, and negative predictive value of 0.857, respectively. Furthermore, adding clinical information did not improve the performance of the DWI/ADC-based deep learning model. The TSS prediction models can be visited at: http://123.57.65.199:3838/deeptss/ . Conclusions: A unique deep learning model based on DWI/ADC radiomic features was constructed for TSS classification, which could aid in decision making for thrombolysis in patients with unknown stroke onset.

Translation and Rotation Analysis Based on Stress MRI for the Diagnosis of Anterior Cruciate Ligament Tears

Publication: Imaging Med Surg. 2022 Jan; 12(1): 257–68. Authors: Klon W, Domżalski M, Malinowski K,Sadlik B Institution: St Luke’s Hospital, Bielsko-Biała, Poland. Abstract: Due to the increasing need for a detailed biomechanical analysis of anterior cruciate ligament (ACL) lesions, the aim of the study was to develop a method of direct measurement of the three-dimensional tibial translation and rotation based on stress MRI. Methods For the purpose of the study, thirty patients with acute ACL rupture and 17 healthy control subjects were selected. Based on clinical examination, they were qualified for MRI examination using the Arthroholder Device prototype to perform anterior tibial translation. Each examination was performed at 30° of knee flexion, initially without tibia translation and then using the force applied to the calf of 80 N. The femur and tibia were separately registered using rigid local SimpleITK landmark refinement; translation and rotation parameters were then calculated using the 3D transformation algorithms. The significance level was set at 0.05. Results Initially, the device and method for obtaining the parameters of the 3D translation and rotation were validated. The pooled Standard Deviation for translation parameters was 0.81 mm and for rotation parameters 0.87°. Compared to the control group, statistically significant differences were found in parameters such as Anterior Shift [(median ± interquartile range) 3.89 mm ±6.55 vs. 0.90 mm ±2.78, P=0.002238] and External Rotation (−0.55° ±3.88 vs. −2.87° ±2.40, P=0.005074). Statistically significant correlations were observed in combined groups between Anterior Shift and parameters such as External Rotation (P=0.001611), PCL Tibial Attachment Point (pPCL) Anterior Shift (<0.000001), Rolimeter Measurement (P=0.000016), and Side-to-Side Difference (SSD) (P=0.000383). A significant statistical correlation was also observed between External Rotation and parameters such as Rolimeter (P=0.02261) and SSD (P=0.03458). Conclusions The analysis of the anterior tibia translation using stress MRI and the proposed three-dimensional calculation method allows for a detailed analysis of the tibial translation and rotation parameters. The correlations showed the importance of external rotation during anterior tibial translation.

The Relationship of LDH and Hematological Parameters with Ischemic Volume and Prognosis in Cerebrovascular Disease

Publication: J Coll Physicians Surg Pak. 2022 Jan;32(1):42-45. PMID: 34983146 Authors: Alatlı T, Kocaoglu S, Akay E. Institution: Department of Emergency, Faculty of Medicine, Balikesir University, Balikesir, Turkey. Abstract: Objective: To determine whether lactate dehydrogenase (LDH), platelet-lymphocyte ratio (PLR), neutrophil-lymphocyte ratio (NLR), and lymphocyte-monocyte ratio (LMR) values can be used as a prediction for their relationship with stroke volume (SV) and for in-hospital mortality in stroke patients in Emergency Department (ED). Study design: Analytical study. Place and duration of study: Balikesir University, Turkey from 24/03/2021 to 30/06/2021. Methodology: Patients aged 18 years or older, diagnosed with stroke in ED, were included in the study. Stroke volumes were calculated from diffusion-weighted images (DWi) with 3D Slicer software using image-based semi-automatic and manual segmentation methods. Results: Of the 265 patients, 128 (48.3%) were males. SV was significantly higher in the non-survivor group than in the survivor group (p=0.007). NLR was significantly higher in the non-survivor group than in the survivor group (p=0.018). Conclusion: The ratios of NLR and SV stand out as practical parameters for the estimation of mortality, prognosis, and management of patients diagnosed with acute stroke. Taking into account, these parameters in the diagnosis process and prognosis management in EDs will provide convenience.