Decision Forests with Long-Range Spatial Context for Organ Localization in CT Volumes

This paper introduces a new, efficient, probabilistic algorithm for the automatic analysis of 3D medical images. Given an input CT volume our algorithm automatically detects and localizes the

anatomical structures within, accurately and efficiently. Our algorithm builds upon randomized decision forests, which are enjoying much success in the machine learning and computer vision communities. This paper extends randomized decision forests by incorporating new, 3D, context-rich visual features, and applies the resulting classifier to the task of automatic parsing of medical images into their parts.

In this paper we focus on detection of human organs, but our general-purpose classifier might be trained instead to detect anomalies and malformations. Applications include (but are not limited to) efficient visualization and navigation through 3D medical scans. The output of our algorithm is probabilistic thus enabling the modeling of uncertainty as well as fusion of multiple sources of information (e.g. multiple modalities). The high level of generalization offered by decision forests yields accurate posterior probabilities for the localization of the organs of interest. High computational efficiency is achieved thanks both to the massive level of parallelism of the classifier as well as the use of integral volumes for feature extraction. The validity of our method is assessed quantitatively on a ground-truth database which has been sanitized by medical experts.

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In  MICCAI workshop on Probabilistic Models for Medical Image Analysis (MICCAI-PMMIA)

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