IPL-Lab-Report 13

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Northeastern University *

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Mechanical Engineering

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Apr 3, 2024

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Manually model the 12th thoracic vertebra from the CT of a person by using 3D slicer Abstract In this experiment, I was asked to simulate myself as a bioengineering intern at a company that designs custom orthopedic shoes. For this internship, I was given the task of manually modeling the 12th thoracic vertebra (T12) from a CT(computed tomography) scan of a client by using 3D slicer. I began by experimenting with manual modeling of the client's upper femur on a CT. The procedure I used was to import the CT scan into 3D slicer, then build the model of the upper femur using 3D slicer, then export the model and record some data. After I was able to do this, I used the same procedure to model T12. After this, I compared the volume from two example groups (86906.9mm^3 with 5793.8mm^3 standard deviation for manual group, 64288.3mm^3 with 11378mm^3 standard deviation for semiautomated group). Then, I created histograms, boxplots, and probplots of the two control groups. Based on the table and the graphs that I got, I will consider the manual group to be more consistent and supporting my opinion because it has a smaller standard deviation than that semiautomated group.

Introduction For this experiment, I worked as a bioengineering co-researcher for a company that designs custom orthotics. Here we were required to use 3D Slicer to design custom orthopedic footwear for injured athletes or athletes training after surgery. This is done by creating bone models using 3D Slicer technology and then using them in the design of custom footwear. The company intends to create a semi- automated modeling process using the 3D slicer extension. The 3D slicer extension will effectively reduce the risk of human error and shorten the modeling turnaround time through automated modeling, such as piece-by-piece removal of non-targeted bones and features from a mask. Our goal was to determine if semi-automated modeling was more accurate and faster to manual modeling. I and 49 other team members were asked to manually model the 12th thoracic vertebra (Th12 or T12) on a 3D slicer over a CT scan, a medical imaging technique used to obtain detailed images of the inside of the body. Since this was my first time using a 3D slicer, I decided to model the upper portion of the femur first to familiarize myself with the specific modeling process, and then model the T12 skeleton. The 3D Slicer uses CT scan images of the client's frontal, sagittal, and cross-sectional views to model the bone. I draw the desired skeletal sections on the CT scan images. I then compare the data from the manual modeling and the data from the other 49 individuals with the results from the semi-automated process where we were given a set of 50 models of the same vertebrae modeled using the 3D slicer. Finally, we compared the two sets of data by using Matlab to draw graphs and perform calculations. Our hypothesis for this experiment was that the semi-automation of the 3D slicer would reduce the errors associated with manual modeling.

Materials & Methods A total of one treatment group and one control group were tested. The materials is a computed tomography(CT) scan of a client and 3D Slicer. 3D Slicer is a tool that being used in image analysis and scientific visualization. In this experiment, it allows us to model the CT scan by drawing on different sections of the CT scan. In treatment group, 50 people who were completely new to 3D Slicer were asked to use it to create 50 models of the 12th thoracic vertebra (T12) of the client in manual. In control group, the semi-automated process was done to model the same vertebra 50 separate times. The modeling procedure is to import the CT scan into 3D slicer, then create two segiments to differentiate between the full bone and the T12 bone, draw the bone model we need in the T12 bone part by painting, and then separate the 3D image of this bone model from the full bone to check the completeness. After the final debugging, the 3D image of the T12 model is exported and its volume is recorded. The semi-automated process used automation of several steps of the modeling process, for example removing non-target bones and features from the mask slice-by-slice. At the end of the modeling process, Matlab was used to compare the volumes of T12 bone obtained from the two groups to create a boxplot, histogram, and probability plot by using boxplot(), bar(), and probplot(). Then, a table with appropriate descriptive statistics (mean, standard deviation) was created by using mean(), std().

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Results As we can see from the boxplot above, the distribution of the manual group is much tighter than the semi-mutomated one and the overall size of the data is larger. Manual Semiautomated 5 6 7 8 9 10 T12 volume(mm 3 ) 10 4 Boxplots of two groups of 50 different T12 volume values each gained from manual and semiautomated F1. Boxplots showing T12 volume values gained from manual and semiautomated

From the histogram above, we saw that Manual's data was more tightly distributed like a hill-shape and tended to be normally distributed, while Semiautomated's data was more severely skewed. Histograms of T12 volume values gained from manual 7.5 8 8.5 9 9.5 10 Manual 10 4 0 2 4 6 8 10 12 14 16 T12 volume(mm 3 ) Histograms of T12 volume values gained from semiautomated 4 5 6 7 8 9 Semiautomated 10 4 0 2 4 6 8 10 12 14 16 18 20 T12 volume(mm 3 ) F2. Histograms showing T12 volume values gained from manual and semiautomated

Through visual analysis, it could be determined that the manual group is normally distributed and the semiautomated seemed less likely to be a normal distribution since there were some obvious outliers. 4 5 6 7 8 9 10 Data 10 4 0.01 0.05 0.1 0.25 0.5 0.75 0.9 0.95 0.99 Probability Probability of two groups of 50 different T12 volume values each gained from manual and semiautomated Manual Semiautomated T1. The mean and standard deviation of T12 volume of manual group and semiautomated group F3. Normal probability plot showing visual representation of normally distributed T12 volume from manual and relatively normally distributed T12 volume from semiautomated

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T12vol_mm3_manual T12vol_mm3_semiautomated Mean 86907 64288 Std 5793.8 11378

Based on the data above, we could clearly see that the standard deviation of the volume of T12 for manual group is relatively smaller to semiautomated group and the mean value is relatively larger. F4. My T12 vertebra model views from axial from the head F5. My T12 vertebra model views from axial from the feet F6. My T12 vertebra model views from coronal from the back F7. My T12 vertebra model views from coronal from the front

F8. My T12 vertebra model views from saggital

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First, by observing from the head and feet which are F4,F5, we can find a hole in the middle of the main body of T12. And we can get an idea of what the T12 looks like. Through the views from coronal(back and front), we can see that there are some bones protruding from the top and sides of T12. And the upper part is partially empty. Finally, through views from saggital, the lower part of T12 is not all linked together, and there is a U-shaped groove in the middle that is empty. The volume of my T12 vertebra model is 63887.9mm^3. Discussion The data we collected in this survey were not consistent with the assumptions that drive semi- automated modeling. First, by comparing the two sets of data, we can see that manual modeling produces results that oppose our goals. By comparing the value of standard deviation and the boxplot distribution of the two groups, we can clearly see that the distribution of T12 volumes presented by the manual modeling is more dense and the standard deviation is smaller. This indicates that manual modeling is more accurate than semi-automated modeling, which is the opposite of our goal. Meanwhile, based on histrograms and normal probability plots, we can clearly see that the data of manual modeling is normally distributed while semi-automated modeling is not. Therefore, we can see that the results of manual modeling are more stable and less prone to errors. This also leads to the fact that our hypothesis is not valid. Our assumption is that semi-automated modeling is more accurate and less error prone than manual modeling. The experimental results are the opposite.

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