A project of the Biomedical Image Analysis group, Department of Biomedical Engineering, Eindhoven, together with Philips Healthcare, Best.PhD student: Henri Bouma (thesis 02-04-2008)Supervisors: prof. Frans Gerritsen, prof. Bart ter Haar Romeny, dr. Anna Vilanova
Blood cloths in the blood stream are
dangerous. When they come into narrower vessels, they may block the flow. When
this occurs in the in the lungs we speak of a pulmonary embolism (PE). There
are more than 50 cases of PE per 100,000 persons every year in the USA. Of
these cases, 11% die in the first hour and in total, the untreated mortality
rate of PE is estimated to be 30% versus 2.5% for appropriately treated PE.
A lung illustration depicting a pulmonary embolism as a thrombus (blood clot) that has travelled from another region of the body, causes occlusion of the pulmonary bronchial artery, leading to arterial thrombosis of the superior and inferior lobes in the left lung. From: Wikipedia.
resolution CT is the preferred diagnostic imaging test. Source: Healthline.
2003, prof. Frans Gerritsen of Philips Healthcare (part-time professor in the
TU/e-BMIA group) asked for assistance to design an automatic image analysis
method for PE from 3D CT. We hired Henri Bouma as a PhD student.
Today we learn features from lots of data
with deep learning, in those days we designed proper features.
PE appears as a filling defect inside the
in CT images (see alto the entry picture
on top of this page).
chart of the computer-aided diagnosis (CAD) system for pulmonary embolism consisting of vessel
candidate detection, feature computation and classification. From Bouma 2008.
One particular feature was
the local vessel shape, for PE in particular the deviation of a
vessel from a 3D tubular shape.
This can be measured from the ‘shape
space’, i.e. the
principal curvatures of a point on a surface.
If we measure the curvature in all
directions from a point, there is a maximum and a minimum curvature, always in
perpendicular directions. These are the principal curvatures, mathematically
found by the eigenvalues of the Hessian matrix, i.e. the second order derivative matrix.
You can play with these principal
curvatures yourself in the interactive plot below.
K1 and K2 are the principal curvatures: change them with the sliders (press the + to see the values. Double click the richt top-arrow bracket to see the code). If both principal curvatures zero: surface is
flat. If one is zero: cylinder. If both are positive: ellipsoid or sphere. If
different sign: saddle point.
Further reading: Henri Bouma (2008),
Vessel-diameter quantification and embolus detection in CTA Images, PhD thesis Eindhoven University of Technology.