Feasibility of automated 3-dimensional magnetic resonance imaging pancreas segmentation.

PurposeWith the advent of MR guided radiotherapy, internal organ motion can be imaged simultaneously during treatment. In this study, we evaluate the feasibility of pancreas MRI segmentation using state-of-the-art segmentation methods.Methods and materialT2 weighted HASTE and T1 weighted VIBE images were acquired on 3 patients and 2 healthy volunteers for a total of 12 imaging volumes. A novel dictionary learning (DL) method was used to segment the pancreas and compared to t mean-shift merging (MSM), distance regularized level set (DRLS), graph cuts (GC) and the segmentation results were compared to manual contours using Dice's index (DI), Hausdorff distance and shift of the-center-of-the-organ (SHIFT).ResultsAll VIBE images were successfully segmented by at least one of the auto-segmentation method with DI >0.83 and SHIFT ≤2 mm using the best automated segmentation method. The automated segmentation error of HASTE images was significantly greater. DL is statistically superior to the other methods in Dice's overlapping index. For the Hausdorff distance and SHIFT measurement, DRLS and DL performed slightly superior to the GC method, and substantially superior to MSM. DL required least human supervision and was faster to compute.ConclusionOur study demonstrated potential feasibility of automated segmentation of the pancreas on MRI images with minimal human supervision at the beginning of imaging acquisition. The achieved accuracy is promising for organ localization

Relationship between primary liver hepatocellular carcinoma volumes on portal-venous phase CT imaging

The liver is an important organ in the body. It is located under the rib cage on the right side. The liver performs many important functions, it processes food for nutrients that the body requires and also helps in the detoxification of harmful materials. Like any organ in the body, the liver is susceptible to diseases such as liver cancer. Liver cancer is the growth and spread of unhealthy cells of the liver. There are several risk factor for liver cancer, these are: Cirrhosis (scarring of the liver), long term hepatitis B and hepatitis C infection and diabetes patients with long term drinking problem. Hepatocellular Carcinoma is the most common form of liver cancer in adult population which begins in the main type of liver cell (hepatocyte). Because Hepatocellular carcinoma starts from the primary liver cell itself (hepatocytes), as such it is a primary liver cancer. About 30,000 Americans are diagnosed with primary liver cancer yearly, making it an important disease that plaques our society and therefore needs proper diagnosis. In clinical evaluation of primary liver cancer such as HCC, the use of medical imaging technology has been commonplace. Most medical facilities across the country and globally typically use Computed Tomography (CT) and/or Magnetic Resonance Imaging (MRI) in the diagnosis and treatment follow up of Hepatocellular carcinoma. The medical imaging devices are used to determine the extent and volume of the tumor of the cancerous liver cells. In clinical trials involving the imaging of HCC tumors, the typical protocol used in the CT imaging of HCC involves the use of contrast enhanced dual phase acquisition. This approach is based on the physiology of the blood flow through the liver. Since HCC tumors are hypervascular in nature, it would thus be more apparent in the arterial phase of an acquired CT image. The aforementioned characteristic was tested with a volume paradigm which measure and compare the volume of both the arterial phase and portal venous phase acquired images in the experiment. Overall this study helps in furthering goals to reduce the patient dose from the x-ray tubes during clinical trials. The results of the experiments (n = 19, t = 0.67, p = 0.26), indicates no significant difference between the volume of the HCC tumor images acquired both in the AP and PVP

Validation of volumetric and single-slice MRI adipose analysis using a novel fully automated segmentation method.

PURPOSE: To validate a fully automated adipose segmentation method with magnetic resonance imaging (MRI) fat fraction abdominal imaging. We hypothesized that this method is suitable for segmentation of subcutaneous adipose tissue (SAT) and intra-abdominal adipose tissue (IAAT) in a wide population range, easy to use, works with a variety of hardware setups, and is highly repeatable. MATERIALS AND METHODS: Analysis was performed comparing precision and analysis time of manual and automated segmentation of single-slice imaging, and volumetric imaging (78-88 slices). Volumetric and single-slice data were acquired in a variety of cohorts (body mass index [BMI] 15.6-41.76) including healthy adult volunteers, adolescent volunteers, and subjects with nonalcoholic fatty liver disease and lipodystrophies. A subset of healthy volunteers was analyzed for repeatability in the measurements. RESULTS: The fully automated segmentation was found to have excellent agreement with manual segmentation with no substantial bias across all study cohorts. Repeatability tests showed a mean coefficient of variation of 1.2 ± 0.6% for SAT, and 2.7 ± 2.2% for IAAT. Analysis with automated segmentation was rapid, requiring 2 seconds per slice compared with 8 minutes per slice with manual segmentation. CONCLUSION: We demonstrate the ability to accurately and rapidly segment regional adipose tissue using fat fraction maps across a wide population range, with varying hardware setups and acquisition methods. J. Magn. Reson. Imaging 2015;41:233-241. © 2014 Wiley Periodicals, Inc

Application of Dual-Energy Computed Tomography to the Evalution of Coronary Atherosclerotic Plaque

Atherosclerotic coronary artery disease is responsible for around 50 of cardiovascular deaths in USA. Early detection and characterization of coronary artery atherosclerotic plaque could help prevent cardiac events. Computed tomography (CT) is an excellent modality for imaging calcifications and has higher spatial resolution than other common non-invasive modalities (e.g MRI), making it more suitable for coronary plaque detection. However, attenuation-based classification of non-calcified plaques as fibrous or lipid is difficult with conventional CT, which relies on a single x-ray energy. Dual-energy CT (DECT) may provide additional attenuation data for the identification and discrimination of plaque components. The purpose of this research was to evaluate the feasibility of DECT imaging for coronary plaque characterization and further, to explore the limits of CT for non-invasive plaque analysis. DECT techniques were applied to plaque classification using a clinical CT system. Saline perfused coronary arteries from autopsies were scanned at 80 and 140 kVp, prior to and during injection of iodinated contrast. Plaque attenuation was measured from CT images and matched to histology. Measurements were compared to assess differences among plaque types. Although calcified and non-calcified plaques could be identified and differentiated with DECT, further characterization of non-calcified plaques was not possible. The results also demonstrated that calcified plaque and iodine could be discriminated. The limits of x-ray based non-calcified plaque discrimination were assessed using microCT, a pre-clinical x-ray based high spatial resolution modality. Phantoms and tissues of different composition were scanned using different tube voltages (i.e., different energies) and resulting attenuation values were compared. Better vessel wall visualization and increase in tissue contrast resolution was observed with decrease in x-ray energy. Feasibility of calcium quantification from contrast-enhanced scans by creating virtual n

Application of Dual-Energy Computed Tomography to the Evalution of Coronary Atherosclerotic Plaque

Atherosclerotic coronary artery disease is responsible for around 50 of cardiovascular deaths in USA. Early detection and characterization of coronary artery atherosclerotic plaque could help prevent cardiac events. Computed tomography (CT) is an excellent modality for imaging calcifications and has higher spatial resolution than other common non-invasive modalities (e.g MRI), making it more suitable for coronary plaque detection. However, attenuation-based classification of non-calcified plaques as fibrous or lipid is difficult with conventional CT, which relies on a single x-ray energy. Dual-energy CT (DECT) may provide additional attenuation data for the identification and discrimination of plaque components. The purpose of this research was to evaluate the feasibility of DECT imaging for coronary plaque characterization and further, to explore the limits of CT for non-invasive plaque analysis. DECT techniques were applied to plaque classification using a clinical CT system. Saline perfused coronary arteries from autopsies were scanned at 80 and 140 kVp, prior to and during injection of iodinated contrast. Plaque attenuation was measured from CT images and matched to histology. Measurements were compared to assess differences among plaque types. Although calcified and non-calcified plaques could be identified and differentiated with DECT, further characterization of non-calcified plaques was not possible. The results also demonstrated that calcified plaque and iodine could be discriminated. The limits of x-ray based non-calcified plaque discrimination were assessed using microCT, a pre-clinical x-ray based high spatial resolution modality. Phantoms and tissues of different composition were scanned using different tube voltages (i.e., different energies) and resulting attenuation values were compared. Better vessel wall visualization and increase in tissue contrast resolution was observed with decrease in x-ray energy. Feasibility of calcium quantification from contrast-enhanced scans by creating virtual n

Deep grey matter volumetry as a function of age using a semi-automatic qMRI algorithm

Quantitative Magnetic Resonance has become more and more accepted for clinical trial in many fields. This technique not only can generate qMRI maps (such as T1/T2/PD) but also can be used for further postprocessing including segmentation of brain and characterization of different brain tissue. Another main application of qMRI is to measure the volume of the brain tissue such as the deep Grey Matter (dGM). The deep grey matter serves as the brain's "relay station" which receives and sends inputs between the cortical brain regions. An abnormal volume of the dGM is associated with certain diseases such as Fetal Alcohol Spectrum Disorders (FASD). The goal of this study is to investigate the effect of age on the volume change of the dGM using qMRI. Thirteen patients (mean age= 26.7 years old and age range from 0.5 to 72.5 years old) underwent imaging at a 1.5T MR scanner. Axial images of the entire brain were acquired with the mixed Turbo Spin-echo (mixed -TSE) pulse sequence. The acquired mixed-TSE images were transferred in DICOM format image for further analysis using the MathCAD 2001i software (Mathsoft, Cambridge, MA). Quantitative T1 and T2-weighted MR images were generated. The image data sets were further segmented using the dual-space clustering segmentation. Then volume of the dGM matter was calculated using a pixel counting algorithm and the spectrum of the T1/T2/PD distribution were also generated. Afterwards, the dGM volume of each patient was calculated and plotted on scatter plot. The mean volume of the dGM, standard deviation, and range were also calculated. The result shows that volume of the dGM is 47.5 ±5.3ml (N=13) which is consistent with former studies. The polynomial tendency line generated based on scatter plot shows that the volume of the dGM gradually increases with age at early age and reaches the maximum volume around the age of 20, and then it starts to decrease gradually in adulthood and drops much faster in elderly age. This result may help scientists to understand more about the aging of the brain and it can also be used to compare with the results from former studies using different techniques

3D Deep Learning on Medical Images: A Review

The rapid advancements in machine learning, graphics processing technologies and availability of medical imaging data has led to a rapid increase in use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, give a brief mathematical description of 3D CNN and the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection, and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models, in general) and possible future trends in the field.Comment: 13 pages, 4 figures, 2 table

Liver Segmentation and Liver Cancer Detection Based on Deep Convolutional Neural Network: A Brief Bibliometric Survey

Background: This study analyzes liver segmentation and cancer detection work, with the perspectives of machine learning and deep learning and different image processing techniques from the year 2012 to 2020. The study uses different Bibliometric analysis methods. Methods: The articles on the topic were obtained from one of the most popular databases- Scopus. The year span for the analysis is considered to be from 2012 to 2020. Scopus analyzer facilitates the analysis of the databases with different categories such as documents by source, year, and county and so on. Analysis is also done by using different units of analysis such as co-authorship, co-occurrences, citation analysis etc. For this analysis Vosviewer Version 1.6.15 is used. Results: In the study, a total of 518 articles on liver segmentation and liver cancer were obtained between the years 2012 to 2020. From the statistical analysis and network analysis it can be concluded that, the maximum articles are published in the year 2020 with China is the highest contributor followed by United States and India. Conclusions: Outcome from Scoups database is 518 articles with English language has the largest number of articles. Statistical analysis is done in terms of different parameters such as Authors, documents, country, affiliation etc. The analysis clearly indicates the potential of the topic. Network analysis of different parameters is also performed. This also indicate that there is a lot of scope for further research in terms of advanced algorithms of computer vision, deep learning and machine learning