R3年度博士論文
題目：Study on Statistical Method for Segmentation of Multi-object on
Abdominal CT Images
氏名：Jiaqi Wu
Computer aided diagnosis (CAD) is the use of a computer-generated output as an auxiliary
tool for the assistance of efficient interpretation and accurate diagnosis. Medical image
segmentation has an essential role in CAD in clinical applications. Generally, the task of
medical image segmentation involves multiple objects, such as organs or diffused tumor
regions. Moreover, it is very unfavorable to segment these regions from abdominal Computed
Tomography (CT) images because of the overlap in intensity and variability in position andshape of soft tissues. In this thesis, a progressive segmentation framework is proposed to extract
liver and tumor regions from CT images more efficiently, which includes the steps of multiple
organs coarse segmentation, fine segmentation, and liver tumors segmentation.
Benefit from the previous knowledge of the shape and its deformation, the Statistical shapemodel (SSM) method is firstly utilized to segment multiple organs regions robustly. In theprocess of building an SSM, the correspondence of landmarks is crucial to the quality of themodel. To generate a more representative prototype of organ surface, a k-mean clustering
method is proposed. The quality of the SSMs, which is measured by generalization ability,specificity, and compactness, was improved. We furtherly extend the shapes correspondence tomultiple objects. A non-rigid iterative closest point surface registration process is proposed toseek more properly corresponded landmarks across the multi-organ surfaces. The accuracy ofsurface registration was improved as well as the model quality. Moreover, to localize theabdominal organs simultaneously, we proposed a random forest regressor cooperating intensity
features to predict the position of multiple organs in the CT image. The regions of the organs
are substantially restrained using the trained shape models. The accuracy of coarse
segmentation using SSMs was increased by the initial information of organ positions.
Consequently, a pixel-wise segmentation using the classification of supervoxels is applied
for the fine segmentation of multiple organs. The intensity and spatial features are extracted
from each supervoxels and classified by a trained random forest. The boundary of thesupervoxels is closer to the real organs than the previous coarse segmentation.
Finally, we developed a hybrid framework for liver tumor segmentation in multiphase
images. To deal with these issues of distinguishing and delineating tumor regions and peripheral
tissues, this task is accomplished in two steps: a cascade region-based convolutional neuralnetwork (R-CNN) with a refined head is trained to locate the bounding boxes that containtumors, and a phase-sensitive noise filtering is introduced to refine the following segmentationof tumor regions conducted by a level-set-based framework. The results of tumor detection
show the adjacent tumors are successfully separated by the improved cascaded R-CNN. The
accuracy of tumor segmentation is also improved by our proposed method.
26 cases of multi-phase CT images were used to validate our proposed method for thesegmentation of liver tumors. The average precision and recall rates for tumor detection are76.8% and 84.4%, respectively. The intersection over union, true positive rate, and false
positive rate for tumor segmentation are 72.7%, 76.2%, and 4.75%, respectively.