Hippocampal subfield plays a significant role in the human body. They are minute neural substance that has gained more interest in the research field. Study of this neural structure has basically been impeded due to its interior location in the human brain. The major challenge is to get clear images around its location using the methods that have been embraced in the scientific field. Due to its small size as well as inability to detect the signals, it has made it difficult to study it. Moreover, the current technology employs the use 3T MR which has low resolution power. Solving the problem of segmentation is solved by the proposed method of 3T multi-modality MR Image. This excellent procedure gives a solution to the unfathomed area, of studying hippocampal subfields, which has been ignored by most of the scientist. It is important to note that 3T multimodality MR images encompasses two methods. One is fMRI, which clearly indicates the structural arrangement of the of the subfields. Ideally, one is able to locate the connectivity patterns of the structure. The second one is structural MRI. Both of them extracts information and feed them into trained classifiers. Once the information is interpreted, it is possible to technically study the connectivity between different subfields. Notably, it is evidently justified that multi-modality features increases the process of segmentation.
Dexterity in human body is such a vital element which cannot be ignored. John (2020) affirms that hippocampal is an essential structure useful in encoding and retrieval of information. If we are able to understand the structure and its components, it becomes easy to control health conditions associated unhealthy hippocampus. Traditional, neural scientist went ahead and tried to understand the entire structure. The effort yielded futile results because it became a bit complex. Segmentation is the only way to get full information which can be helpful in diagnosing neural related diseases. This traditional method of examining the entire structure is replaced by a current one, which seeks to deconstruct the process into smaller units of observation. What makes segmentation an important trend is because change of hippocampal volume can be a good indication of a neurological disease? Subfield are valuable in directing the possible areas of disease origination.
Many applications for segmentation have been brought forward. However, most of them are manual. This means that they would require an expert to operate and would also lead to time wastage. Although automatic is the solution to this problem, it has its own hurdles as well. One is the problem of resolution and low signal which are unable to succinctly give identifiable features. To solve this problem associated with automatic segmentation, two strategies are proposed. One is performing multiple scans to increase the chances of reliability. Additionally, dedication of scan protocols would give more accurate.
Getting real meaning of the data has a lot to be analyzed from the automatic methods. Extraction of the data to capture connectivity is an essential step which cannot be ignored. To achieve this, a few question have to be asked. One, how do you select reference region? How do you extract related features? In order to explore reference regions, the assumption which is normally made is that the bold signals have greater correlation in areas where the subfield are within the same region. If there is greater deviation, then the subfield are in different locations. Ultimately, the direct result gotten from the use of multi-modality method is precision of the point of focus. It is worth noting this method is more stable compared to other available ones. The images produced are clear, a less overlapping is witnessed.
Moreover, the images produced by the current method appears less bumpy. This is an advantage to the radiologist since he or she is able to deliantes and identify areas of interest. Applying the method in the current form of diagnoses would give the medical PR actioners a more elaborate manner to address certain complication which could not be established through traditional methods. From the dataset of results obtained from the multimodality images, it is possible to compare the information and improve segmentation. Along, automatic method can be able to compare both appearance and features.Embracing the new move is the right away to go.
Notably, the previous paragraphs indicate that there are two broad sections of segmenting the hippocampus. Basically, the automatic one performs better by far compared to the novice second rater. Training for the second rater takes a little bit longer time as opposed to automatic. With the invention of the automatic segmentations, this time can be considerably reduced, to increase the level of accuracy. It has to be remembered that the effectiveness of any diagnosis or research work is actualized by the rate at which studies are accomplished. If one structure is studied for a long time due to inefficient methods, it not only affects the output but also impedes the validity of data. This underlines the importance of automation, since it handled large data set in areas where manual could be prohibitive.
Consequently any method is embraced depending on its reliability scale. For the current automatic method of segmenting the subfield, it was discovered that it had comparable higher accuracy. It is important to state here any medical adventures focuses on accuracy and ability to identify every single lead information. Settling on a method which can be able to clearly show every characteristic of the organ under study remains paramount. The use of 3T MR has incorporated machine learning corrective recognition. This is whereby the machine is used to show images with the description of every structure surrounding the primary organ of interest. Under the technological real, it is regarded as artificial intelligence. The machine possess the capability to do tasks which manual procedures cannot afford to accomplish. With the rapid changes in the medical field, it is expected that more sophisticated and efficient methods of analysis are underway. It is the long tradition of inclining to traditional believes of past methods, which has continually impeded the positive growth in segmentation.
Settling for multimodality method of segmentation is the only way which guarantee accuracy of information. The method compares with the available methods, which have not proven efficient in the task. Little has been researched on 3T MRI, but it sounds as the best solution. The move will assist greatly in establishing some of the neurological diseases early. Considering the high precision rate, considerable interest has been shifted to this process of segmentation. In the near future, it is expected that the manual methods will be faced off. Alternatively, both automatic and manual will be used corporately to supplement the accuracy levels. More research should be conducted on multi-modality and suggest areas of improvement.
Bankman, I. N. (2009). Handbook of medical image processing and analysis. Boston: Academic Press.
Beichel, R. R., Sonka, M., CVAMIA, ECCV, CVAMIA, Computer Vision Approaches to Medical Image Analysis Workshop, ECCV … European Conference on Computer Vision. (2006). Computer vision approaches to medical image analysis: Second international ECCV workshop, CVAMIA 2006, Graz, Austria, May 12, 2006; revised papers. Berlin [u.a.: Springer.
Celebi, M. E. (2014). Color medical image analysis. Place of publication not identified: Springer.
Cong, S., Risacher, S. L., West, J. D., Wu, Y., Apostolova, L. G., Tallman, E. F., … Shen, L. (2016). VOLUMETRIC COMPARISON OF AUTOMATICALLY SEGMENTED HIPPOCAMPAL SUBFIELDS FROM 4-MIN ACCELERATED VERSUS 8-MIN T2-WEIGHTED 3T MRI SCANS. Alzheimer’s & Dementia, 12(7), P1167.
Costaridou, L. (2005). Medical image analysis methods. Boca Raton: CRC Press/Taylor & Francis.
Giuliano, A., Donatelli, G., Cosottini, M., Tosetti, M., Retico, A., &Fantacci, M. E. (2017). Hippocampal subfields at ultra high field MRI: An overview of segmentation and measurement methods. Hippocampus, 27(5), 481-494.
Hillman, B. J., & Goldsmith, J. C. (2011). The sorcerer’s apprentice: How medical imaging is changing health care. New York: Oxford University Press.
Marizzoni, M., Nobili, F., Didic, M., Bartres, D., Fiedler, U., Schönknecht, P., Jovicich, J. (2015). Multi-site hippocampal subfields reproducibility: A European 3T study. Alzheimer’s & Dementia, 11(7), P558.
Santos, F., F. Smagula, S., Karim, H., S. Santini, T., J. Aizenstein, H., S. Ibrahim T., & D. Maciel, C. (2017). Dynamic Bayesian Network Modeling of Hippocampal Subfields Connectivity with 7T fMRI: A Case Study. Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies.
Sherrow, V. (2007). Medical imaging. New York: Marshall Cavendish Benchmark.
Toennies, K. D. (2017). Guide to medical image analysis: Methods and algorithms.
Witharana, W., Cardiff, J., Chawla, M., Xie, J., Alme, C., Eckert, M.McNaughton, B. (2016). Nonuniform allocation of hippocampal neurons to place fields across all hippocampal subfields. Hippocampus, 26(10), 1328-1344.
Winterburn, Julie, Pruessner, Jens C, Sofia, Chavez, … M Mallar. (2015). High-resolution In Vivo manual segmentation protocol for human hippocampal subfields using 3T magnetic resonance imaging.
Wolf, D., Fischer, F. U., De Flores, R., Chételat, G., & Fellgiebel, A. (2015). Differential associations of age with volume and microstructure of hippocampal subfields in healthy older adults. Human Brain Mapping, 36(10), 3819-3831.
Wu, Z., Gao, Y., Shi, F., Jewells, V., & Shen, D. (2016). Automatic Hippocampal Subfield Segmentation from 3T Multi-modality Images. Machine Learning in Medical Imaging, 229-236.
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