In histopathological classification and diagnosis of cancer cases, pathologists perform visual assessments of immunohistochemistry (IHC)-stained biomarkers in cells to determine tumor versus non-tumor tissues. One of the prerequisites for such assessments is the correct identification of regions-of-interest (ROIs) with relevant histological features. Advances in image processing and machine learning give rise to the possibility of full automation in ROI identification by identifying image features such as colors and textures. Such computer-aided diagnostic systems could enhance research output and efficiency in identifying the pathology (normal, non-tumor or tumor) of a tissue pattern from ROI images. In this paper, a computational method using color-texture based extreme learning machines (ELM) is proposed for automatic tissue tumor classification. Our approach consists of three steps: (1) ROIs are manually identified and annotated from individual cores of tissue microarrays (TMAs); (2) color and texture features are extracted from the ROIs images; (3) ELM is applied to the extracted features to classify the ROIs into non-tumor or tumor categories. The proposed approach is tested on 100 sets of images from a kidney cancer TMA and the results show that ELM is able to achieve classification accuracies of 91.19% and 88.72% with a Gaussian radial basis function (RBF) and linear kernel, respectively, which is superior to using SVM with the same kernels.
After surgical repair for Tetralogy of Fallot (TOF), most patients experience long-term complications as the right ventricle (RV) undergoes progressive remodeling that eventually affect heart functions. Thus, post-repair surgery is required to prevent further deterioration of RV functions that may result in malignant ventricular arrhythmias and mortality. The timing of such post-repair surgery therefore depends crucially on the quantitative assessment of the RV functions. Current clinical indices for such functional assessment measure global properties such as RV volumes and ejection fraction. However, these indices are less than ideal as regional variations and anomalies are obscured. Therefore, we sought to (i) develop a quantitative method to assess RV regional function using regional ejection fraction (REF) based on a 13-segment model, and (ii) evaluate the effectiveness of REF in discriminating 6 repaired TOF patients and 6 normal control based on cardiac magnetic resonance (CMR) imaging. We observed that the REF for the individual segments in the patient group is significantly lower compared to the control group (P < 0.05 using a 2-tail student t-test). In addition, we also observed that the aggregated REF at the basal, mid-cavity and apical regions for the patient group is significantly lower compared to the control group (P < 0.001 using a 2-tail student t-test). The results suggest that REF could potentially be used as a quantitative index for assessing RV regional functions. The computational time per data set is approximately 60 seconds, which demonstrates our method’s clinical potential as a real-time cardiac assessment tool.
In this work, we develop an automatic method to generate a set of 4D 1-to-1 corresponding surface meshes of the left ventricle (LV) endocardial surface which are motion registered over the whole cardiac cycle. These 4D meshes have 1- to-1 point correspondence over the entire set, and is suitable for advanced computational processing, such as shape analysis, motion analysis and finite element modelling. The inputs to the method are the set of 3D LV endocardial surface meshes of the different frames/phases of the cardiac cycle. Each of these meshes is reconstructed independently from border-delineated MR images and they have no correspondence in terms of number of vertices/points and mesh connectivity. To generate point correspondence, the first frame of the LV mesh model is used as a template to be matched to the shape of the meshes in the subsequent phases. There are two stages in the mesh correspondence process: (1) a coarse matching phase, and (2) a fine matching phase. In the coarse matching phase, an initial rough matching between the template and the target is achieved using a radial basis function (RBF) morphing process. The feature points on the template and target meshes are automatically identified using a 16-segment nomenclature of the LV. In the fine matching phase, a progressive mesh projection process is used to conform the rough estimate to fit the exact shape of the target. In addition, an optimization-based smoothing process is used to achieve superior mesh quality and continuous point motion.
After myocardial infarction (MI), the left ventricle (LV) undergoes progressive remodeling which adversely affects heart function and may lead to development of heart failure. There is an escalating need to accurately depict the LV remodeling process for disease surveillance and monitoring of therapeutic efficacy. Current practice of using ejection fraction to quantitate LV function is less than ideal as it obscures regional variation and anomaly. Therefore, we sought to (i) develop a quantitative method to assess LV regional ejection fraction (REF) using a 16-segment method, and (ii) evaluate the effectiveness of REF in discriminating 10 patients 1-3 months after MI and 9 normal control (sex- and agematched) based on cardiac magnetic resonance (CMR) imaging. Late gadolinium enhancement (LGE) CMR scans were also acquired for the MI patients to assess scar extent. We observed that the REF at the basal, mid-cavity and apical regions for the patient group is significantly lower as compared to the control group (P < 0.001 using a 2-tail student t-test). In addition, we correlated the patient REF over these regions with their corresponding LGE score in terms of 4 categories – High LGE, Low LGE, Border and Remote. We observed that the median REF decreases with increasing severity of infarction. The results suggest that REF could potentially be used as a discriminator for MI and employed to measure myocardium homogeneity with respect to degree of infarction. The computational performance per data sample took approximately 25 sec, which demonstrates its clinical potential as a real-time cardiac assessment tool.
KEYWORDS: Magnetic resonance imaging, Heart, Cardiovascular magnetic resonance imaging, Magnetism, Americium, Tissues, Mechanics, Data processing, Image segmentation, In vivo imaging
Ischemic dilated cardiomyopathy (IDCM) is a degenerative disease of the myocardial tissue accompanied by left ventricular (LV) structural changes such as interstitial fibrosis. This can induce increased passive stiffness of the LV wall. However, quantification of LV passive wall stiffness in vivo is extremely difficult, particularly in ventricles with complex geometry. Therefore, we sought to (i) develop a computer-based assessment of LV passive wall stiffness from cardiac magnetic resonance (CMR) imaging in terms of a nominal stiffness index (E*); and (ii) investigate whether E* can offer an insight into cardiac mechanics in IDCM. CMR scans were performed in 5 normal subjects and 5 patients with IDCM. For each data sample, an in-house software was used to generate a 1-to-1 corresponding mesh pair of the LV from the ED and ES phases. The E* values are then computed as a function of local ventricular wall strain. We found that E* in the IDCM group (40.66 – 215.12) was at least one order of magnitude larger than the normal control group (1.00 – 6.14). In addition, the IDCM group revealed much higher inhomogeneity of E* values manifested by a greater spread of E* values throughout the LV. In conclusion, there is a substantial elevated ventricular stiffness index in IDCM. This would suggest that E* could be used as discriminator for early detection of disease state. The computational performance per data sample took approximately 25 seconds, which demonstrates its clinical potential as a real-time cardiac assessment tool.
This paper presents an approach to gallbladder shape comparison by using 3D shape modeling and decomposition. The
gallbladder models can be used for shape anomaly analysis and model comparison and selection in image guided robotic
surgical training, especially for laparoscopic cholecystectomy simulation. The 3D shape of a gallbladder is first
represented as a surface model, reconstructed from the contours segmented in CT data by a scheme of propagation based
voxel learning and classification. To better extract the shape feature, the surface mesh is further down-sampled by a
decimation filter and smoothed by a Taubin algorithm, followed by applying an advancing front algorithm to further
enhance the regularity of the mesh. Multi-scale curvatures are then computed on the regularized mesh for the robust
saliency landmark localization on the surface. The shape decomposition is proposed based on the saliency landmarks
and the concavity, measured by the distance from the surface point to the convex hull. With a given tolerance the 3D
shape can be decomposed and represented as 3D ellipsoids, which reveal the shape topology and anomaly of a
gallbladder. The features based on the decomposed shape model are proposed for gallbladder shape comparison, which
can be used for new model selection. We have collected 19 sets of abdominal CT scan data with gallbladders, some
shown in normal shape and some in abnormal shapes. The experiments have shown that the decomposed shapes reveal
important topology features.
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