Leveraging Mid-Level Semantic Boundary Cues for Automated Lymph Node Detection View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2015

AUTHORS

Ari Seff , Le Lu , Adrian Barbu , Holger Roth , Hoo-Chang Shin , Ronald M. Summers

ABSTRACT

Histograms of oriented gradients (HOG) are widely employed image descriptors in modern computer-aided diagnosis systems. Built upon a set of local, robust statistics of low-level image gradients, HOG features are usually computed on raw intensity images. In this paper, we explore a learned image transformation scheme for producing higher-level inputs to HOG. Leveraging semantic object boundary cues, our methods compute data-driven image feature maps via a supervised boundary detector. Compared with the raw image map, boundary cues offer mid-level, more object-specific visual responses that can be suited for subsequent HOG encoding. We validate integrations of several image transformation maps with an application of computer-aided detection of lymph nodes on thoracoabdominal CT images. Our experiments demonstrate that semantic boundary cues based HOG descriptors complement and enrich the raw intensity alone. We observe an overall system with substantially improved results (~78% versus 60% recall at 3 FP/volume for two target regions). The proposed system also moderately outperforms the state-of-the-art deep convolutional neural network (CNN) system in the mediastinum region, without relying on data augmentation and requiring significantly fewer training samples. More... »

PAGES

53-61

References to SciGraph publications

Book

TITLE

Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015

ISBN

978-3-319-24570-6
978-3-319-24571-3

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-24571-3_7

DOI

http://dx.doi.org/10.1007/978-3-319-24571-3_7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1050944423


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