ERPs and alpha oscillations track the encoding and maintenance of object-based representations in visual working memory

When memorizing an integrated object such as a Kanizsa figure, the completion of parts into a coherent whole is attained by grouping processes which render a whole-object representation in visual working memory (VWM). The present study measured event-related potentials (ERPs) and oscillatory amplitudes to track these processes of encoding and representing multiple features of an object in VWM. To this end, a change detection task was performed, which required observers to memorize both the orientations and colors of six "pacman" items while inducing configurations of the pacmen that systematically varied in terms of their grouping strength. The results revealed an effect of object configuration in VWM despite physically constant visual input: change detection for both orientation and color features was more accurate with increased grouping strength. At the electrophysiological level, the lateralized ERPs and alpha activity mirrored this behavioral pattern. Perception of the orientation features gave rise to the encoding of a grouped object as reflected by the amplitudes of the Ppc. The grouped object structure, in turn, modulated attention to both orientation and color features as indicated by the enhanced N1pc and N2pc. Finally, during item retention, the representation of individual objects and the concurrent allocation of attention to these memorized objects were modulated by grouping, as reflected by variations in the CDA amplitude and a concurrent lateralized alpha suppression, respectively. These results indicate that memorizing multiple features of grouped, to-be-integrated objects involves multiple, sequential stages of processing, providing support for a hierarchical model of object representations in VWM.

Object-Oriented Segmentation of Cell Nuclei in Fluorescence Microscopy Images

Cell nucleus segmentation remains an open and challenging problem especially to segment nuclei in cell clumps. Splitting a cell clump would be straightforward if the gradients of boundary pixels in-between the nuclei were always higher than the others. However, imperfections may exist: inhomogeneities of pixel intensities in a nucleus may cause to define spurious boundaries whereas insufficient pixel intensity differences at the border of overlapping nuclei may cause to miss some true boundary pixels. In contrast, these imperfections are typically observed at the pixel-level, causing local changes in pixel values without changing the semantics on a large scale. In response to these issues, this article introduces a new nucleus segmentation method that relies on using gradient information not at the pixel level but at the object level. To this end, it proposes to decompose an image into smaller homogeneous subregions, define edge-objects at four different orientations to encode the gradient information at the object level, and devise a merging algorithm, in which the edge-objects vote for subregion pairs along their orientations and the pairs are iteratively merged if they get sufficient votes from multiple orientations. Our experiments on fluorescence microscopy images reveal that this high-level representation and the design of a merging algorithm using edge-objects (gradients at the object level) improve the segmentation results.