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Found 7 results

  1. There are many challenging cases, in which the single segmentation is not enough. The paranasal sinuses and the congenital heart defects are notable examples. My usual workflow was to segment whatever I can as good as it's possible, to clean the unnecessary structures and the artefacts, to export the segmentation as stl 3d model and then to "CAD my way around". This is solid philosophy for simple, uncomplicated models, but for complex structures with a lot of small details and requirement from the client for the highest quality possible, this is just not good enough, especially for a professional anatomist like myself. Then I started to exploit the simple fact, that you're actually able to export the model as stl, to model it with your CAD software and then to reimport it back and convert it into label map again. I called this "back and forth technique". You can model the finest details on your model and then you can continue the segmentation right where you need it, catching even the slightest details of the morphology of the targeted structure. This technique, combined with my expertise, gives me the ability to produce the best possible details on some of the most challenging cases, including nasal cavity, heart valves, brain models etc. etc.To use this technique, just import the stl file, convert it into a label map (for 3D slicer - segmentation module/ export/import models and label maps). The main advantages of this technique are:1. You can combine the segmentation with the most advanced CAD functions of your favorite software. Two highly specialized programs are better than one "Jack of all trades" (cough cough Mimics cough cough)2. Advanced artefact removing.3. Advanced small detail segmentation and modelling.4. Combined with several markers (separate segmentations, several voxels in size) on the nearby anthropometric points, this technique increases the accuracy of the final product significantly. Without points of origin, the geometry of your model will go to hell, if you're not especially careful (yes, I'm talking about the 3D brushes in Slicer).5. You can easily compare the label map with the 3d model, converted back. Every deviation, produced during the CAD operations will be visible like a big, shining dot, which you can easily see and correct. This is one of the strongest quality control techniques.6. You can create advanced masks with all the geometrical forms you can possibly imagine, which you can use for advanced detail segmentation. Those masks will be linked with the spatial coordinates of the targeted structures - the stl file preserves the exact coordinates of every voxel, which was segmented.7. You can go back and forth multiple times, as many as you like.8. This technique is more powerful than the best AI, developed by now. It combines the best from the digital technologies with the prowess of the human visual cortex (the best video card up to date).The main disadvantages are:1. It's time consuming.2. It produces A LOT of junk files.3. Advanced expertise is needed for this technique. This is not some "prank modelling", but an actual morphological work. A specialized education and practical experience in the human anatomy, pathology and radiology will give you the best results, which this technique can offer. 4. You need highly developed visual cortex for this technique (dominant visual sense). This technique is not for the linguistic, spatial-motor, olphactory etc. types of brains. Recent studies confirms, that a part of the population have genetically determined bigger, more advanced visual cortex (The human connectome project, Prof. David Van Essen, Washington University in Saint Louis). Such individuals become really successful cinematographers, designers, photographers and medical imaging specialists. The same is true for all the other senses, but right now we're talking about visual modality and 3D intellect (I'm sorry, dear linguists, musicians, craftsmen and tasters). It's not a coincidence that I have so many visual artists in my family (which makes me the medical black sheep). But if you don't have this kind of brain, you can still use the technique for quality control and precise mask generation. Just let the treshould module or the AI to do the job for you in the coordinates, in which you want (You should really start using the Segment Editor module in Slicer 3D).5. You really need to love your work, if you're using this technique. For the usual 3D modelling you don't need so many details in your model and to "CAD your way around" is enough for the task.6. You should use only stl files. For some reason, the obj format can't preserve the spatial geometry as good as the stl format. Maybe because the stl is just a simple map of vertex coordinates and the obj contains much more sophisticated data. The simple, the better.On the picture - comparison of the semilunar valves, made by treshould segmentation at 250-450 Hounsfield units (in green) and modelled and reimported model (in red).
  2. Hello the Biomedical 3D Printing community, it's Devarsh Vyas here writing after a really long time! This time i'd like to share my personal experience and challenges faced with respect to medical 3D Printing from the MRI data. This can be a knowledge sharing and a debatable topic and I am looking forward to hear and know what other experts here think of this as well with utmost respect. In the Just recently concluded RSNA conference at Chicago had a wave of technology advancements like AI and 3D Printing in radiology. Apart from that the shift of radiologists using more and more MR studies for investigations and the advancements with the MRI technology have forced radiologists and radiology centers (Private or Hospitals) to rely heavily on MRI studies. We are seeing medical 3D Printing becoming mainstream and gaining traction and excitement in the entire medical fraternity, for designers who use the dicom to 3D softwares, whether opensource or FDA approved software know that designing from CT is fairly automated because of the segmentation based on the CT hounsifield units however seldom we see the community discuss designing from MRI, Automation of segmentation from MRI data, Protocols for MRI scan for 3D Printing, Segmentation of soft tissues or organs from MRI data or working on an MRI scan for accurate 3D modeling. Currently designing from MRI is feasible, but implementation is challenging and time consuming. We should also note reading a MRI scan is a lot different than reading a CT scan, MRI requires high level of anatomical knowledge and expertise to be able to read, differentiate and understand the ROI to be 3D Printed. MRI shows a lot more detailed data which maybe unwanted in the model that we design. Although few MRI studies like the contrast MRI of the brain, Heart and MRI angiograms can be automatically segmented but scans like MRI of the spine or MRI of the liver, Kidney or MRI of knee for example would involve a lot of efforts, expertise and manual work to be done in order to reconstruct and 3D Print it just like how the surgeon would want it. Another challenge MRI 3D printing faces is the scan protocols, In CT the demand of high quality thin slices are met quite easily but in MRI if we go for protocols for T1 & T2 weighted isotropic data with equal matrix size and less than 1mm cuts, it would increase the scan time drastically which the patient has to bear in the gantry and the efficiency of the radiology department or center is affected. There is a lot of excitement to create 3D printed anatomical models from the ultrasound data as well and a lot of research is already being carried out in that direction, What i strongly believe is the community also need advancements in terms of MRI segmentation for 3D printing. MRI, in particular, holds great potential for 3D printing, given its excellent tissue characterization and lack of ionizing radiation but model accuracy, manual efforts in segmentation, scan protocols and expertise in reading and understanding the data for engineers have come up as a challenge the biomedical 3D printing community needs to address. These are all my personal views and experiences I've had with 3D Printing from MRI data. I'm open to and welcome any tips, discussions and knowledge sharing from all the other members, experts or enthusiasts who read this. Thank you very much!
  3. So I have seen some questions here on embodi3D asking how to work with MRI data. I believe the main issue to be with attempting to segment the data using a threshold method. The democratiz3D feature of the website simplifies the segmentation process but as far as I can tell relies on thresholding which can work somewhat well for CT scans but for MRI is almost certain to fail. Using 3DSlicer I show the advantage of using a region growing method (FastGrowCut) vs threshold. The scan I am using is of a middle aged woman's foot available here The scan was optimized for segmenting bone and was performed on a 1.5T scanner. While a patient doesn't really have control of scan settings if you are a physician or researcher who does; picking the right settings is critical. Some of these different settings can be found on one of Dr. Mike's blog entries. For comparison purposes I first showed the kind of results achievable when segmenting an MRI using thresholds. With the goal of separating the bones out the result is obviously pretty worthless. To get the bones out of that resultant clump would take a ridiculous amount of effort in blender or similar software: If you read a previous blog entry of mine on using a region growing method I really don't like using thresholding for segmenting anatomy. So once again using a region growing method (FastGrowCut in this case) allows decent results even from an MRI scan. Now this was a relatively quick and rough segmentation of just the hindfoot but already it is much closer to having bones that could be printed. A further step of label map smoothing can further improve the rough results. The above shows just the calcaneous volume smoothed with its associated surface generated. Now I had done a more proper segmentation of this foot in the past where I spent more time to get the below result If the volume above is smoothed (in my case I used some of my matlab code) I can get the below result. Which looks much better. Segmenting a CT scan will still give better results for bone as the cortical bone doesn't show up well in MRI's (why the metatarsals and phalanges get a bit skinny), but CT scans are not always an option. So if you have been trying to segment an MRI scan and only get a messy clump I would encourage you to try a method a bit more modern than thresholding. However, keep in mind there are limits to what can be done with bad data. If the image is really noisy, has large voxels, or is optimized for the wrong type of anatomy there may be no way to get the results you want.
  4. Version 1.0.0

    13 downloads

    Bronchi, dicom, stl, ct without contrast, segmentation, lunhg, heart, axial, dicom

    Free

  5. Version 1.0.0

    2 downloads

    coarction aorta, dicom, segmentation, thorax, axial, ct with contrast

    Free

  6. I wanted to take some time to look into a brief history of medical image segmentation before moving into what I consider the more modern method of segmentation. (be warned video is rather long) First to be clear the goal of segmentation is to separate the bones or anatomy of interest from 3D scan data. This is done most easily when there is a sharp contrast between the anatomy of interest and what surrounds it. If you have a CT scan of an engine block this is pretty straight forward. The density of metal to air is hard to beat, but for anatomy and especially MRI scans this is a whole other story. Anatomical boundaries are often more of gradients than sharp edges. Over the years there have been many approaches to make the process of segmenting anatomy faster, easier, and less subjective then the dreaded 'manual segmentation'. When I first started working with medical images back around 2003 the group I was at was trying an alternative to their previous method. Their previous method involved using ImageJ to separate each bone of the foot by applying a threshold then going in and 'fixing' that by painting... They wanted to segment the bones of the foot and it would take like 10 hours of tedious labor... fortunately that was before my time. I was tasked with figuring out how to get 3DViewNix to work. It was basically a research project that ran on linux (which I hadn't used before). Its had a special algorithm called 'live-wire' which allowed clicking on a few points around the edge of the bone on each slice to get a closed contour that matched the bone edge then doing that for each scan slice for each bone. This resulted in about 3 hours a foot of still rather mind numbing effort. After a while a radiologist with a PhD electrical engineering student let us know that there were much better ways. His student had some software written in IDL that allowed using 'seeds' in each bone that would then grow out in 3D to the edges of the bones. After some time to get setup we were able to segment a foot in less than an hour with a good portion of that being computer time. My background is as an ME so I don't pretend to fully understand the image processing algorithms but I have used them in various forms. This year I got more familiar with 3DSlicer which I have found to be the best open source medical imaging program yet. It is built off of VTK and ITK and has a very nice GUI making seeding far more convenient (other programs I've used didn't really allow zooming). It took me a while to find something similar to what I had used before but eventually I found the extension 'FastGrowCut' gives very good results, enough to move away from the special software I had been using before that wasn't free. My basic explanation of 'FastGrowCut' and similar region growing algorithms is; you start with 'seeds' which are labeled voxels for the different anatomy of interest. The algorithm then grows the seeds until it reaches the edge of the bone/anatomy or a different growing seed. There is then a back and forth until it stabilizes on what the edge really is. The result is a 'label' file which has all the voxels labeled as background or one of the entities of interest. Once everything is segmented to the level that you like I prefer to do volumetric smoothing of each entity (bone) before creating the surface models. These algorithms are an active area of research typically in image processing groups within university electrical engineering departments. The algorithms are not a silver bullet that works on all situations, there are a variety of other methods (some as extensions to 3DSlicer) for specific situations. Thin features, long tubular features, noisy data (metal artifacts), low quality scans (scouts), will still take more time and effort to get good results. No algorithm can take a low resolution, low quality scan and give you a nice model... garbage in = garbage out. Now I have been surprised bemoaned to find thresholding used as a common segmentation technique, often as the main tool even in expensive commercial programs. That style typically involves applying a threshold then going in and cleaning up the model until you get something close to what you want. To me this seems rather antiquated but for quickly viewing data or creating a quick and rough model it really can't be beat... but for creating high quality models to be printed there are better ways.
  7. From the album: ebaumel Blog images

    Segmantation of a coronal MRI image of a kidney with a renal mass.

    © Copyright ©2015 Eric M. Baumel, MD

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