3D Segmentation Algorithms for Computerized Tomographic Imaging: a Systematic Literature Review.

This paper presents a systematic literature review concerning 3D segmentation algorithms for computerized tomographic imaging. This analysis covers articles published in the range 2006-March 2018 found in four scientific databases (Science Direct, IEEEXplore, ACM, and PubMed), using the methodology for systematic review proposed by Kitchenham. We present the analyzed segmentation methods categorized according to its application, algorithmic strategy, validation, and use of prior knowledge, as well as its general conceptual description. Additionally, we present a general overview, discussions, and further prospects for the 3D segmentation methods applied for tomographic images.

Cognitive Performance of Patients with Epilepsy and Calcified Neurocysticercotic Lesions: A Case-Control Study.

Neuropsychological tests were applied to 20 patients with focal epilepsy related to calcified neurocysticercosis (NCC) (mean: three lesions/patient; NCC group), 22 patients with focal epilepsy without NCC (EPI group), and 29 healthy controls matched for age, sex, and educational level. The EPI and NCC groups were matched for age at onset of epilepsy, epilepsy duration, frequency of attacks, seizure semiology, interictal EEG findings, and antiepileptic drugs used. There were no differences in the digit span, word span, calculus, and Mini-Mental State examination among the three groups studied. The NCC and EPI groups showed lower scores than controls in immediate and delayed verbal memory, famous faces test, spatial recognition span, abstractions and judgment, and visuoconstructional abilities. The EPI group, but not the NCC group, also had lower scores in a praxis tests. There were no differences between the NCC and EPI groups in any of the tests applied (P > 15), except for the spatial recognition span, which was lower in the former. Cognitive impairment is a prevalent neuropsychological feature of patients with epilepsy and NCC.

Data-driven approaches to decision making in automated tumor grading. An example of astrocytoma grading.

OBJECTIVE: To compare four data-driven approaches to automated tumor grading based on morphometric data. Apart from the statistical procedure of linear discriminant analysis, three other approaches from the field of neural computing were evaluated. STUDY DESIGN: The numerical basis of this study was computed tomography-guided, stereotactically obtained astrocytoma biopsies from 86 patients colored with a combination of Feulgen and immunhistochemical Ki-67 (MIB1) staining. In these biopsies the cell nuclei in four consecutive fields of vision were evaluated morphometrically and the following parameters determined: relative nuclei area, secant lengths of the minimal spanning trees and relative volume-weighted mean nuclear volumes of the proliferating nuclei. RESULTS: Based on the analysis of these morphometric features, the multivariate-generated HOM grading system provides the highest correct grading rates (> 90%), whereas the two widely employed qualitative histologic grading systems for astrocytomas yield correct grading rates of about 60%. For automated tumor grading all approaches yield similar grading results; however, back-propagation networks provide reliable results only following an extensive training phase, which requires the use of a supercomputer. All other neurocomputing models can be run on simple UNIX workstations (AT&T, U.S.A). CONCLUSION: In contrast to discriminant analysis, backpropagation and Kohonen networks, the newly developed neural network architecture model of self-editing nearest neighbor nets (SEN3) provides incremental learning; i.e., the training phase does not need to be restarted each time when there is further information to learn. Trained SEN3 networks can be considered ready-to-use knowledge bases and are appropriate to integrating further morphometric data in a dynamic process that enhances the diagnostic power of such a network.

Automated grading of astrocytomas based on histomorphometric analysis of Ki-67 and Feulgen stained paraffin sections. Classification results of neuronal networks and discriminant analysis.

In stereotactically obtained astrocytoma biopsies, four morphometric nuclear parameters were determined with the use of an image analysis system. A special Ki-67 (MIB1)/Feulgen stain made it possible to quantify the essential characteristics of gliomas of the astrocytoma/glioblastoma group: growth pattern, cellularity, proliferation tendency and nucleus pleomorphism. A grading scale based on a cluster analysis resembling the WHO-scheme, which is suitable for automated astrocytoma grading, was developed. Large back propagation neural networks were used and their results compared with those of a classical multivariate discriminant classification analysis. It is possible to show that the neural network technology is superior to the statistical approach for automated astrocytoma grading. Based on the results of our study we believe neural network technology to be useful for tumour grading problems. The presented approach can be generalized for the automated grading of other tumour entities.

[Computer assisted grading of gliomas of the astrocytoma/glioblastoma groups]. / Computergestützte Graduierung von Gliomen der Astrocytom/Glioblastom-Gruppe.

For automated astrocytoma grading morphometric parameters are determined by means of an image analysis system and a special Ki-67(MIB1)/Feulgen-staining method allowing the quantification of the essential characteristics of malignant gliomas: growth pattern, cellularity, proliferation index and nucleus pleomorphism. Based upon a cluster analytical approach a grading scale resembling the WHO-scheme is established which is suitable for automatic glioma grading purposes (HOM-scale). For automatic glioma grading backpropagation neural networks are employed. The results are compared with those of a classical multivariate discriminant classificatory analysis. The presented approach can also be employed for automatic grading of other tumour entities.