Research Article: Automatically measuring brain ventricular volume within PACS using artificial intelligence

Date Published: March 15, 2018

Publisher: Public Library of Science

Author(s): Fernando Yepes-Calderon, Marvin D. Nelson, J. Gordon McComb, Han-Chiao Isaac Chen.

http://doi.org/10.1371/journal.pone.0193152

Abstract

The picture archiving and communications system (PACS) is currently the standard platform to manage medical images but lacks analytical capabilities. Staying within PACS, the authors have developed an automatic method to retrieve the medical data and access it at a voxel level, decrypted and uncompressed that allows analytical capabilities while not perturbing the system’s daily operation. Additionally, the strategy is secure and vendor independent. Cerebral ventricular volume is important for the diagnosis and treatment of many neurological disorders. A significant change in ventricular volume is readily recognized, but subtle changes, especially over longer periods of time, may be difficult to discern. Clinical imaging protocols and parameters are often varied making it difficult to use a general solution with standard segmentation techniques. Presented is a segmentation strategy based on an algorithm that uses four features extracted from the medical images to create a statistical estimator capable of determining ventricular volume. When compared with manual segmentations, the correlation was 94% and holds promise for even better accuracy by incorporating the unlimited data available. The volume of any segmentable structure can be accurately determined utilizing the machine learning strategy presented and runs fully automatically within the PACS.

Partial Text

The Picture Archiving and Communications System (PACS) is currently the standard platform to manage medical images [1] but lacks analytical and quantification capabilities [2, 3]. Staying within the PACS, the authors have developed an automatic method to retrieve the medical data and access it at a voxel level, decrypted and uncompressed that enables analytical procedures to be applied to the data while not perturbing the system’s daily operation. Additionally, the strategy is secure and vendor independent [4].

Manual segmentations of the ten studied images selected were performed for comparison with the automatic ventricular volume estimator (AVVE). In this comparison, the volume differences and the Jaccard index [28] are used. The volume differences define the overall discrepancies between manual and automatic assessments. The Jaccard index says how well the two compared structures overlap. When compared with its manual counterpart, the AVVE underestimated the volumes in all cases, except in the one where an arachnoid cyst adjacent to the ventricles misled the algorithm, see panel d in Fig 6.

The machine learning implementation proposed here reached a 94% of accuracy and it holds promises to improve further in the light of the unlimited amount of available data in the PACS with which to learn. Also, other meaningful features can be considered in the future, including those associated with age factors that are available in the DICOM headers within the PACS [29].

A common clinical neurological problem is optimally managing hydrocephalus for which many MR/CT studies are done to determine if the ventricular volume has changed. It is anticipated that soon, the neuroradiology report will routinely include a number indicating ventricular volume. This technique can be applied to any cerebral structure that can be segmented such as GM,WM, various nuclei, tumors, aneurysms, etc. Treatment effect can be better monitored and characterized if one is able to measure any volume change accurately.

 

Source:

http://doi.org/10.1371/journal.pone.0193152

 

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