AutomatedLocalization ofFetalOrgans inMRIUsingRandomForestswithSteerableFeatures

K. Keraudren1, B. Kainz1, O. Oktay1, V. Kyriakopoulou2,M. Rutherford2, J.V. Hajnal2, D. Rueckert1

1 Biomedical Image Analysis Group, Imperial College London, 2 Centre for the Developing Brain, King’s College London

Size normalizationDue to fetal motion, fetal MRI is typically ac-quired as stacks of 2D slices of real-time MRI,freezing in-plane motion. Motion correctionmethods can subsequently be applied to cor-rect the misalignment between slices and pro-vide consistent 3D data1. Such methods performslice-to-volume registration and require the fetalanatomy to be isolated from surrounding mater-nal tissues. We thus propose a method to auto-matically localize the fetal heart, lungs and liver.

~u0~u0

~w0~w0

~v0~v0

brainbrain

heartheart

Sagittal plane

~v0~v0

~w0~w0

~u0~u0

heartheart

rightrightlunglung

Transverse plane

Average image of the training data aftersize normalization.

To reduce the variability due to fetal develop-ment, we normalize the size of all fetuses by re-sampling the images to an isotropic voxel sizesga that is a function of the gestational age, sothat a fetus of 30 weeks is resampled to a voxelsize s30: sga = CRLga/CRL30 × s30 where CRLdenotes the crown-rump length.

Organ localization pipelineInspired by Hough Forests2, the proposed method performs classification (a,c) and regression (b,d)steps using Random Forests in order to assign voxels to a certain organ then vote for the location ofthe organ center. A set of organ candidates is generated, then scored based on their relative position.

(a) (b) (c) (d)

Proposed pipeline for the automated localization of fetal organs in MRI.

The center of the brain3 is first used to steer features when detecting the heart, which then fixes anaxis when detecting the lungs and liver. Knowing the location of the brain, the search for the heartonly needs to explore the image region contained between two spheres. The search for the lungs andliver can similarly be restricted to a sphere around the heart.

Steerable featuresIn order to cope with the unknown orientationof the fetus, image features are extracted in alocal coordinate system. At training time, thecoordinate system (~u0, ~v0, ~w0) is defined by land-marks on the fetal anatomy. At test time, thecoordinate system (~u,~v, ~w) is estimated as or-gans are detected: first the brain, which fixes apoint, then the heart, which fixes an axis, andfinally the liver and both lungs.

~u~u~v~v

~u~u~v~v

~u~u

~v~v

At test time, features are steered towardthe center of the brain.

ResultsThe method was evaluated on two datasets of T2 MRI, a first dataset without motion artifacts anda second with artifacts, with gestational ages (GA) ranging from 20 to 38 weeks. Thanks to the sizenormalization, the same trained detector can be used across all GA. A similar performance of thedetector across GA was observed. In 90% of cases, the detected heart center is within 10mm of theground truth, which suggests that the proposed method could provide an automated initializationfor the motion correction of the chest4. The asymmetry in the performance of the detector betweenthe left and right lungs can be explained by the presence of the liver below the right lung, leading toa stronger geometric constraint when ranking organ candidates.

Leftlung

Rightlung

Heart Liver0

5

10

15

20

25

30

35

40

Dis

tanc

eer

ror

(mm

)

1st dataset: healthy

Leftlung

Rightlung

Heart Liver0

5

10

15

20

25

30

35

401st dataset: IUGR

Leftlung

Rightlung

Heart Liver Brain0

5

10

15

20

25

30

35

402nd dataset

Distance error between the predicted organ centers and their ground truth for the firstdataset (30 healthy and 25 IUGR fetuses) and the second dataset (64 healthy fetuses).

References[1] M. Kuklisova-Murgasova, G. Quaghebeur,

M. Rutherford, J. Hajnal, and J. Schnabel,“Reconstruction of Fetal Brain MRI with Inten-sity Matching and Complete Outlier Removal,”Medical Image Analysis, 2012.

[2] J. Gall and V. Lempitsky, “Class-specific HoughForests for Object Detection,” in CVPR, 2009.

[3] K. Keraudren, V. Kyriakopoulou, M. Ruther-ford, J. V. Hajnal, and D. Rueckert, “Localisa-tion of the Brain in Fetal MRI Using BundledSIFT Features,” in MICCAI, 2013.

[4] B. Kainz, C. Malamateniou, M. Murgasova,K. Keraudren, M. Rutherford, J. V. Hajnal, andD. Rueckert, “Motion Corrected 3D Reconstruc-tion of the Fetal Thorax from Prenatal MRI,” inMICCAI, 2014.

Conclusion & Future work

Acquisition

Motioncorrection

Sagittal Coronal Transverse

We presented a pipeline which, in combinationwith automated brain detection3, enables the au-tomated localization of the lungs, heart and liverin fetal MRI. The localization results can be usedto initialize a segmentation or motion correction,and to orient the 3D volume with respect to thefetal anatomy to facilitate clinical diagnosis.Preliminary work used the rough segmentationproduced by the detection process to generate amask for the fetal trunk, with morphological op-erations and a region growing algorithm. Futurework will focus on a slice-by-slice segmentationin order to increase the quality of the motion cor-rected volume.