Main Page/SlicerCommunity/2022

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

  • 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.


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.

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.

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