RESUMEN
Accurate differentiation of intramedullary spinal cord tumors and inflammatory demyelinating lesions and their subtypes are warranted because of their overlapping characteristics at MRI but with different treatments and prognosis. The authors aimed to develop a pipeline for spinal cord lesion segmentation and classification using two-dimensional MultiResUNet and DenseNet121 networks based on T2-weighted images. A retrospective cohort of 490 patients (118 patients with astrocytoma, 130 with ependymoma, 101 with multiple sclerosis [MS], and 141 with neuromyelitis optica spectrum disorders [NMOSD]) was used for model development, and a prospective cohort of 157 patients (34 patients with astrocytoma, 45 with ependymoma, 33 with MS, and 45 with NMOSD) was used for model testing. In the test cohort, the model achieved Dice scores of 0.77, 0.80, 0.50, and 0.58 for segmentation of astrocytoma, ependymoma, MS, and NMOSD, respectively, against manual labeling. Accuracies of 96% (area under the receiver operating characteristic curve [AUC], 0.99), 82% (AUC, 0.90), and 79% (AUC, 0.85) were achieved for the classifications of tumor versus demyelinating lesion, astrocytoma versus ependymoma, and MS versus NMOSD, respectively. In a subset of radiologically difficult cases, the classifier showed an accuracy of 79%-95% (AUC, 0.78-0.97). The established deep learning pipeline for segmentation and classification of spinal cord lesions can support an accurate radiologic diagnosis. Supplemental material is available for this article. © RSNA, 2022 Keywords: Spinal Cord MRI, Astrocytoma, Ependymoma, Multiple Sclerosis, Neuromyelitis Optica Spectrum Disorder, Deep Learning.
RESUMEN
During the dyeing process in baths approximately 10 to 15% of the dyes used are lost and reach industrial effluents, thus polluting the environment. Studies showed that some classes of dyes, mainly azo dyes and their by-products, exert adverse effects on humans and local biota, since the wastewater treatment systems and water treatment plants were found to be ineffective in removing the color and reducing toxicity of some dyes. In the present study, the toxicity of the azo dyes disperse orange 1 (DO1), disperse red 1 (DR1), and disperse red 13 (DR13) was evaluated in HepG2 cells grown in monolayers or in three dimensional (3D) culture. Hepatotoxicity of the dyes was measured using 3-(4,5-dimethylthiazol-2yl)2,5-diphenyltetrazolium (MTT) and cell counting kit 8 (CCK-8) assays after 24, 48, and 72 h of incubation of cells with 3 different concentrations of the azo dyes. The dye DO1 only reduced the mitochondrial activity in HepG2 cells grown in a monolayer after 72 h incubation, while the dye DR1 showed this deleterious effect in both monolayer and 3D culture. In contrast, dye DR13 decreased the mitochondrial activity after 24, 48, and 72 h of exposure in both monolayer and 3D culture. With respect to dehydrogenase activity, only the dye DR13 diminished the activity of this enzyme after 72 h of exposure in both monolayer and 3D culture. Our results clearly demonstrated that exposure to the studied dyes induced cytotoxicity in HepG2 cells.
Asunto(s)
Compuestos Azo/toxicidad , Colorantes/toxicidad , Hepatocitos/efectos de los fármacos , Alginatos , Compuestos Azo/química , Colorantes/química , Ácido Glucurónico , Células Hep G2 , Ácidos Hexurónicos , Humanos , Pruebas de Mutagenicidad , Mutágenos/toxicidad , Contaminantes Químicos del Agua/química , Contaminantes Químicos del Agua/toxicidadRESUMEN
In this paper, a network of coupled chaotic maps for pixel clustering is proposed. Time evolutions of chaotic maps in the network corresponding to a pixel cluster are synchronized with each other. Those synchronized trajectories are desynchronized with respect to the time evolutions of chaotic maps corresponding to other pixel clusters in the same image. A pixel motion mechanism is also introduced, which makes each group of pixels more compact and, consequently, makes the model robust enough to classify ambiguous pixels. Another feature of the proposed model is that the number of pixel clusters does not need to be previously known.
RESUMEN
This article addresses the investigation of the relationship between neural shape and function in cat retinal ganglion cells in terms of representative morphological features. More specifically, a series of geometrical measures is extracted from two-dimensional images of these cells, and pattern recognition methods are applied in order to quantify the differentiation between the two classes (i.e., alpha, beta). The morphological measures cover several of the more meaningful geometrical features of neuronal cells, including: (a) the distribution of angles along the cell contours considering several smoothing degrees; (b) the overall interaction between the cell arborization and the surrounding space, quantified in terms of the multiscale fractal dimension; and (c) the distribution of width and extent of the dendritic processes. Several combinations of such morphological measures are assessed with respect to the separability of the classes. The obtained results indicate that the methods based on statistic relation between segment length and segment diameter, and the method of multiscale angle entropy not only successfully encapsulated a large amount of experimental data into relatively compact patterns but also marked off various ganglion cells into befit groups. On the other hand, the method of neuron classification based on fractal dimension resulted relatively less effective for class separation.