Automatically measuring brain ventricular volume within PACS using artificial intelligence

<div><p>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.</p></div

Normalized distances feature.

<p>On the left, a figure depicting the ND concept. Every voxel is assigned its distance to the scanner central voxel (SCV). In the figure at the right, we have colored the ND feature creating a fading fashioned effect. Here it is evident how every voxel in and out of the learning masks is overlayed with a different number. Although this feature creates separation, voxels radially equidistant with different intensity value would be difficult to assess for any statistical learning algorithm. Therefore, another feature to eliminate this possible ambiguity is needed.</p

Learning and performing the segmentation processes.

<p>The features are extracted for each voxel in the training images, and a support vector machine creates the separating hyperplane. Once the separating hyperplane is created, the automatic segmentation in any new subject is performed by extracting the same features used in the training process and the statistical estimator.</p

The neighboring feature.

<p>The Neig concept (left) creates boundaries where the voxels are too different in intensity. In the normal brain (right), its contribution is nil. However, in case of abnormalities or the presence of other elements inside the ventricles such a CSF diverting shunt, this feature provides meaningful information.</p

The cardinality feature.

<p>In the card feature concept (left), the whole volume is divided into units bigger than a voxel, and each division is assigned a consecutive number. In the card example shown in the right, the grid size has been exaggerated for visualization purposes. Note how there is not only differentiation in the row, but also fading colors at a column level. This FOV demarcation voids the learning discrepancy presented in the ND feature regarding its potential radial ambiguity.</p

Before and after shunt procedure masks.

<p>Manual (cyan) and AVVE (magenta) delineations are overlapped. See companion video for a 360° visualization.</p

Overlapping between manual (cyan) and AVVE (magenta) segmentations.

<p>These axial views obviate big portions of the surface. For a complete reference, see the companion video. The large discrepancy in (d) is secondary to an arachnoid cyst containing CSF adjacent to the occipital horn of the right lateral ventricle.</p

Histogram classified intensity feature.

<p>In panel A, the enveloping signal (EnvS) of the histogram is extracted. In panel B. The inflection points of the EnvS are detected using the positive peaks of the power of five of the second derivate. Panel C shows the estimated parcellation in the original EnvS. Panel D shows an axial slice of healthy neonate that presents low contrast (left) and its parcellation (right).</p

Comparison between manually and automatic extracted volumes.

<p>AVVE stands for Automatic Ventricular Volume Estimator.</p

Patient Characteristics.

<p>Patient Characteristics.</p