Atlas-Based Segmentation of Abdominal Organs in 3D Ultrasound, and its Application in Automated Kidney Segmentation

Transcription

1 University of Toronto Atlas-Based Segmentation of Abdominal Organs in 3D Ultrasound, and its Application in Automated Kidney Segmentation Authors: M. Marsousi, K. N. Plataniotis, S. Stergiopoulos Presenter: M. Marsousi, M. Sc., Ph.D. Candidate University of Toronto Electrical and Computer Eng. Communication Group

2 Agenda Introduction Problem Definition Motivation Why Using 3D Ultrasound Imaging? Challenges of 3D Ultrasound Segmentation Prior Arts Proposed Solution Objectives and Contributions Training Processes Specially Aligned Classifiers Training Process of SANNs Segmentation Process Automated Organ s Shape Segmentation Experiments and Results Conclusion 2

3 Section I. Introduction

4 Problem Definition Developing a fully automated method to segment abdominal organs in 3D ultrasound images. **In particular, segmenting the kidney shape.** Computer Aided Diagnosis (CAD) 4

5 Motivation Abdominal Trauma detection Abdominal Trauma is an internal bleeding. To save a trauma patient s life, rapid diagnosis is required. Internal bleeding is detectable around the right kidney in ultrasound images. Ultrasound Image of internal bleeding [7] 5

6 Why Using 3D Ultrasound Imaging? Advantages of 3D US over CT & MRI Non-invasiveness: 3D US does not expose any danger Portability: unstable patients are not required to be moved Near real-time imaging: essential for emergency situations. Advantages of 3D Ultrasound over 2D Ultrasound Images are visualized which could not be achieved by 2D US 3D US provides localization of internal organs 6

7 Challenges of 3D Ultrasound Segmentation Ultrasound-Specific Challenges: Speckle noise Low Contrast Inconsistent Intensity Profile Gaps among the organ shape s boundary Organ Specific Challenge: Partial visibility of the organ shape Operator-Specific Challenge: Probe misalignment 7

8 Prior Arts Prior arts, addressing kidney segmentation in 3D ultrasound images: Semi-automated: MRF-AC by Fernandez and Lopez [1] Fully-automated: Noll et al. [2] (Automated) Marsousi et al.-embc2014[3] (Automated) [1] Martın-Fernández, M., & Alberola-Lopez, C. (2005). An approach for contour detection of human kidneys from ultrasound images using Markov random fields and active contours. Medical Image Analysis, 9(1), [2] M. Noll, X. Li, and S. Wesarg, Automated kidney detection and segmentation in 3d ultrasound, in Clinical Image-Based Proc. Translational Research in Medical Imag. Springer, 2014, pp [3] M. Marsousi, K. N. Plataniotis, and S. Stergiopoulos, Shape-based kidney detection and segmentation in three-dimensional abdominal ultrasound images, in Proc. IEEE Eng. Med. Biol. Soc., Aug 2014, pp

9 Section II. Methodology

10 Objective and Contributions Objective: Developing a fully-automated method to segment abdominal organs shapes of interest in 3D ultrasound images. Proposed Solution: Applying feature-based registration to fit an input ultrasound data on the reference organ s shape (to improve voxels classification accuracy), Applying spatially aligned neural network classifiers (SANNs) to classify voxels into organ and non-organ classes, based on texture information, Applying affine transformation to fit an organ s shape model on the classified voxels, Using the fitted organ shape model to initialize region-based level-set. 10

11 Methodology Processes: (1) Training, and (2) Segmentation Training ultrasound volumes Reference Ultrasound volume Training Process Organ s Shape Model SANN Classifiers Input ultrasound volume Segmentation Process Detection Segmentation Segmented Organ s shape Organ s shape exists or not 11

12 Training Process Selecting a training set of ultrasound volumes, and a reference volume Registering training volumes on the reference volume Manually outlining organ s shapes Extracting texture features by 3D Gabor filters for organ and non-organ voxels Training spatially aligned neural network classifiers (SANNs) Generating an organ s shape model 12

13 Specially Aligned Classifiers Objective: To improve classification performance by reducing data complexity of each classifier (segment) For each training volume: Register the volume on the reference volume. Extract Gabor features of organ s voxels for each segment. Extract Gabor features of non-organ voxels of the entire volumes. Training a neural network classifier for each segment. B x + W x(k x 1) N x + 1 B y + W y(k y 1) N y + 1 B z + W z(k z 1) N z + 1 K x [1,2,, N x ] K y [1,2,, N y ] K z [1,2,, N z ] x < B x + W x(k x + 1) N x + 1 y < B y + W y K y + 1 N y + 1 z < B z + W z K z + 1 N z

14 Training Process of SANNs Horizontal Concatenation Of feature matrices Vertical Concatenation Of feature vectors 14

15 Segmentation Process Reducing speckle noise using Gaussian FIR filtering, Registering an input ultrasound volume on the reference volume, Extracting texture features using the 3D Gabor filters, Classifying voxels into organ and non-organ candidates, Registering the shape model on the candidate organ s voxels Deciding whether the organ s shape exists or not. Initializing a level-set function using the fitted shape model. 15

16 Automated Organ s Shape Segmentation Organ Shape Detection (Required for Automated Process) Organ Shape Segmentation 16

17 Section III Experiments and Results

18 Experiments and Results Objective Evaluating the proposed kidney detection and segmentation methods Comparing the obtained results with the prior arts Case Study The right upper quadrant (RUQ) view, in which the right kidney is visualized Dataset 36 ultrasound volumes: 21 RUQ volumes with-kidney, and 15 non-ruq views without-kidney volumes. Training set: Containing 6 with-kidney volumes. Evaluation set: Containing 15 with-kidney and 15 without-kidney volumes. Evaluation Metrics Organ detection accuracy: ACC KD = 100%( N TP+N TN ) 30 Organ segmentation metrics: Dice s coefficient DSC = Accuracy ACC = 100% 2TP 2TP+FN+FP TP+TN TP+TN+FP+FN Mean distance MD = 1 AS p AS e p, GT dp Where e p, GT is the l-2 norm 18

19 Organ Detection Results According to TABLE I, the proposed method of this paper shows a higher accuracy compared to the other methods. The proposed method has a high specificity of kidney detection, by making zero False-Positive detection. 19

20 Organ Segmentation Results The proposed method of this paper shows the highest segmentation accuracy, compared to the other methods. 20

21 Conclusion We proposed a fully automated organ segmentation method to segment internal organ s in 3D Ultrasound images. We trained an atlas model of the organ of interest The SANNs classify voxels into organ and non-organ classes, providing a higher detection accuracy. The proposed method was used to segment the kidney shape in ultrasound volumes. The reported results validates the utility of the method in segmenting internal organs in 3D ultrasound images. 21

22 The End, Thank You