8 research outputs found
UOLO - automatic object detection and segmentation in biomedical images
We propose UOLO, a novel framework for the simultaneous detection and
segmentation of structures of interest in medical images. UOLO consists of an
object segmentation module which intermediate abstract representations are
processed and used as input for object detection. The resulting system is
optimized simultaneously for detecting a class of objects and segmenting an
optionally different class of structures. UOLO is trained on a set of bounding
boxes enclosing the objects to detect, as well as pixel-wise segmentation
information, when available. A new loss function is devised, taking into
account whether a reference segmentation is accessible for each training image,
in order to suitably backpropagate the error. We validate UOLO on the task of
simultaneous optic disc (OD) detection, fovea detection, and OD segmentation
from retinal images, achieving state-of-the-art performance on public datasets.Comment: Publised on DLMIA 2018. Licensed under the Creative Commons
CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0
Brain-inspired robust delineation operator
In this paper we present a novel filter, based on the existing COSFIRE
filter, for the delineation of patterns of interest. It includes a mechanism of
push-pull inhibition that improves robustness to noise in terms of spurious
texture. Push-pull inhibition is a phenomenon that is observed in neurons in
area V1 of the visual cortex, which suppresses the response of certain simple
cells for stimuli of preferred orientation but of non-preferred contrast. This
type of inhibition allows for sharper detection of the patterns of interest and
improves the quality of delineation especially in images with spurious texture.
We performed experiments on images from different applications, namely the
detection of rose stems for automatic gardening, the delineation of cracks in
pavements and road surfaces, and the segmentation of blood vessels in retinal
images. Push-pull inhibition helped to improve results considerably in all
applications.Comment: Accepted at Brain-driven Computer Vision workshop at ECCV 201