Burms, Jeroen

Dambre, Joni

Degrave, Jonas

Korshunova, Iryna

English

Ghent University Academic Bibliography

Using Deep Learning to Estimate Systolic and Diastolic volumesfrom MRI-imagesJonas Degrave Jonas.Degrave@Ugent.beJeroen Burms Jeroen.Burms@Ugent.beIryna Korshunova Iryna.Korshunova@ugent.beJoni Dambre Joni.Dambre@Ugent.beGhent University, Technologiepark 15, 9052 Zwijnaarde, BelgiumKeywords: Deep Learning, MRI, Diagnostics, Ejection FractionAbstractDeep learning with convolutional neural net-works has become a widely used tool for com-puter vision tasks. In this paper, we focuson the problem of automatically annotatingthe volumes of the left ventricle in the heart,based on DICOM MRI-images. We discussthe solution, which was the second best in aninternational data mining competition. Weshow that it is possible to achieve near-humanperformance using a deep learning approachwhen using task-specific model architectures.1. IntroductionThe goal of this year’s Data Science Bowl1 was to es-timate minimum (end-systolic) and maximum (end-diastolic) volumes of the left ventricle from a set ofMRI-images taken over one heartbeat. These volumesare used by practitioners to compute an ejection frac-tion: the fraction of outbound blood pumped from theheart with each heartbeat. This measurement can pre-dict a wide range of cardiac problems. For a skilledcardiologist, analysis of MRI scans can take up totwenty minutes. Therefore, automating this processwould be useful to decrease the cost of medical care.2. DatasetThe dataset consisted of over a thousand patients, only500 of which where provided as initial training set. Foreach patient, we were given a number of 30-frame MRIvideos in the DICOM format, such as shown in Fig-ure 1, showing the heart during a single cardiac cycle.These videos were taken in different planes including1http://www.datasciencebowl.com/Figure 1. On the left: an example of an MRI-image in thedataset. The crosses show the intersections between theimages taken over multiple axes. On the right, a cut outversion of this MRI-image after focusing on the ROI usingour preprocessing method.the multiple short-axis views, a 2-chamber view, anda 4-chamber view. The SAX views, whose planes areperpendicular to the long axis of the left ventricle, forma series of slices that (ideally) cover the entire heart.The number of SAX slices ranged from 1 to 23. Typi-cally, the region of interest (ROI) is only a small partof the entire image.Given a patient’s data, we were asked to output a cu-mulative distribution function over the volume, rang-ing from 0 to 599 ml, for both systole and diastole. Themodels were scored by a Continuous Ranked Proba-bility Score (CRPS) error metric, which computes theaverage squared distance between the predicted CDFand a Heaviside step function representing the real vol-ume.Using Deep Learning to Estimate Systolic and Diastolic volumes from MRI-imagesFigure 2. Our preprocessing method took the Fourier transform of the intensity of every pixel over time to extract theperiodically changing pixels. In this Fourier image, it used the circle Hough transform to find the ventricles. It finallycalculated a likelihood surface from which we could find the ROI around the ventricle of interest.3. MethodsIn our solution, we combined traditional image pro-cessing approaches like the one illustrated in Figure 2,which find the region of interest (ROI) in each slice,with convolutional neural networks(Krizhevsky et al.,2012), which perform the mapping from the extractedimage patches to the predicted volumes. Given thevery limited number of training samples, we combatedoverfitting by restricting our models to only combinethe data sources in predefined ways.Figure 3. An illustration of one of the models in our ensem-ble. The red layers are convolution layers, the blue layersare maxpooling layers and the pink layer on top is a spe-cially designed layer which does a frustum approximationbetween the results of the previous layers.Finally, a key insight that led to our approach wasthat it would have been a bad idea to try and makeone model that is capable of processing every singlepatient, given the large amount of inconsistencies inthe data. Instead, we combined accurate models thatassume clean data (SAX slices nicely ranged from oneTable 1. RMS-error of various results of our approach com-pared to that of human annotators. (Lower is better)Diastolic Systolic EjectionVolume Volume FractionCNN 13.65 10.43 6.99%Human 13 14 6%end of the heart to the other) with more robust butless accurate models to handle the outliers. One ofthese models is illustrated in Figure 3.4. ResultsWe finished the Kaggle competition in second placewithout hand-labeling either test or train data. Asyou may find in Table 1, our results perform equallywell as human annotators, confirming the relevance ofdeep learning approaches for computer vision tasks,even on medical data.AcknowledgmentsThe research leading to these results has received fundingfrom the Agency for Innovation by Science and Technologyin Flanders (IWT) and the Research Foundation Flanders(FWO). The Tesla K40 used for this research was donatedby the NVIDIA Corporation.ReferencesKrizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Im-agenet classification with deep convolutional neural net-works. Advances in neural information processing sys-tems (pp. 1097–1105).

Using deep learning to estimate systolic and diastolic volumes from MRI-images

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Using deep learning to estimate systolic and diastolic volumes from MRI-images

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