HEp-2 Cell Classification via Combining Multiresolution Co-Occurrence Texture and Large Region Shape Information

AbstractIndirect immunofluorescence imaging of human epithelial type 2 (HEp-2) cell image is an effective evidence to diagnose autoimmune diseases. Recently, computer-aided diagnosis of autoimmune diseases by the HEp-2 cell classification has attracted great attention. However, the HEp-2 cell classification task is quite challenging due to large intraclass and small interclass variations. In this paper, we propose an effective approach for the automatic HEp-2 cell classification by combining multiresolution co-occurrence texture and large regional shape information. To be more specific, we propose to: 1) capture multiresolution co-occurrence texture information by a novel pairwise rotation-invariant co-occurrence of local Gabor binary pattern descriptor; 2) depict large regional shape information by using an improved Fisher vector model with RootSIFT features, which are sampled from large image patches in multiple scales; and 3) combine both features. We evaluate systematically the proposed approach on the IEEE International Conference on Pattern Recognition (ICPR) 2012, the IEEE International Conference on Image Processing (ICIP) 2013, and the ICPR 2014 contest datasets. The proposed method based on the combination of the introduced two features outperforms the winners of the ICPR 2012 contest using the same experimental protocol. Our method also greatly improves the winner of the ICIP 2013 contest under four different experimental setups. Using the leave-one-specimen-out evaluation strategy, our method achieves comparable performance with the winner of the ICPR 2014 contest that combined four features

Ultrathin silicon nitride microchip for in situ/operando microscopy with high spatial resolution and spectral visibility

Utilization of in situ/operando methods with broad beams and localized probes has accelerated our understanding of fluid-surface interactions in recent decades. The closed-cell microchips based on silicon nitride (SiNx) are widely used as "nanoscale reactors" inside the high-vacuum electron microscopes. However, the field has been stalled by the high background scattering from encapsulation (typically ~100 nanometers) that severely limits the figures of merit for in situ performance. This adverse effect is particularly notorious for gas cell as the sealing membranes dominate the overall scattering, thereby blurring any meaningful signals and limiting the resolution. Herein, we show that by adopting the back-supporting strategy, encapsulating membrane can be reduced substantially, down to ~10 nanometers while maintaining structural resiliency. The systematic gas cell work demonstrates advantages in figures of merit for hitherto the highest spatial resolution and spectral visibility. Furthermore, this strategy can be broadly adopted into other types of microchips, thus having broader impact beyond the in situ/operando fields.</p

Epithelioid Sarcoma and Unclassified Sarcoma with Epithelioid Features: Clinicopathological Variables, Molecular Markers, and a New Experimental Model

This study aims to address the current knowledge gap in the diagnosis and treatment of epithelioid sarcoma (ES) and unclassified sarcoma with epithelioid features (USEF) by: (a) characterizing and comparing the clinical behavior and outcome of ES and USEF patients treated at a single institution, (b) evaluating the expression of a panel of differentiation and other tumor-related molecular markers in human ES and USEF specimens, and (c) developing an ES experimental model for future studies to enhance our understanding of ES molecular determinants while developing more effective therapeutic strategies