Liver Segmentation with Semi-Supervised Learning

Efficient liver segmentation from volume data provides important assistance for minimal invasive surgery and treatment. However, this task suffers from the special anatomy and topological changes. This paper presents a robust interactive method, which treats it as a semi-supervised learning task. An initial classification is performed to partition the volume data into homogeneous blocks to guide the segmentation. It is easy to implement and a more general linear or nonlinear model can be formed by virtue of semi-supervised learning. Experimental results demonstrate the performance of the proposed scheme in liver contours extracting. Introduction Traditional surgical planning of liver uses volumetric information stored in a stack of intensity-based images usually from computerized tomography (CT) and MRI scanners [1]. Based on a number of image slices, surgeons build 3D model of liver, tumor, and vasculature. Quickly and accurately digitized contour model is the basic premise of the surgical simulation and biomechanical evaluation in liver minimal invasive surgery. To move, arrange and remove individual part in a 3D model, each object contour need to be accurately extracted [2]. This task is difficult, even for experienced surgeons. Moreover, tumors can create anatomical variability, which further complicates the situation in volumetric data sets. As a consequence, surgeons can miss important information or draw incorrect conclusions, which can lead to suboptimal treatment strategy decisions. Because complete liver 3D structure is crucial in computer-aided diagnostic [3–4], volume segmentation has been intensively applied with guaranteed results [5]. To deal with the liver segmentation problem, numerous algorithms have been investigated [6]. Commonly, a proper shape or intensity prior is beneficial to separate individual object and create virtual boundaries from surrounding tissues. Level set method maybe a nice solution for this complex problem in theory. However, the scheme is very sensitive to the initialization and suffers from the convergence to the local minima. Gao and Chae [7] proposed an active contour tracking algorithm to separate the touching tissues and adaptively deal with the topological changes, which can obtain individual model with higher accuracy but needed more manual interventions. Gang Chen and Li xu Gu [8] proposed a novel multiple-initialization LSM approach to overcome the leakage and over-segmentation problems in segmenting the liver region from MRIs. Xing Zhang and Jie Tian [9] present an approach for automatic liver segmentation from computed tomography (CT) scans that is based on a statistical shape model (SSM) integrated with an optimal-surface-detection strategy, but requires a multi-dimension parameter space and high computational cost. Because contour extracting in liver image suffers from topological changes, this fact makes liver segmentation is a challenging issue [10]. A novel semi-automatic algorithm is proposed to efficiently achieve the desired contours through a simple strategy. Firstly, the volume data is partitioned into homogeneous blocks by 3D mean shift classification [11]. Then, user interaction is performed in the selected MPR (Multi-Planar Reformation) image planes to indicate the desired object and background, i.e. initialize the labeled 4th National Conference on Electrical, Electronics and Computer Engineering (NCEECE 2015) © 2016. The authors Published by Atlantis Press 312 and unlabeled instances as the inputs of subsequent semi-supervised learning (SSL). The object contours can be extracted by labeling all the blocks as either object or background (labeled as 1 and 0). The rest of the paper is organized as follows. Section 2 lists the previous works. Section 3 presents the proposed semi-supervised learning based algorithm. Section 4 performs the experiment to verify the proposed method. Section 5 concludes the paper. Previous Works The efficiency of user interaction is important to guarantee the precision. A Cellular Automaton (CA) based method [12] has the merits of few interventions, which can balance the contradiction of precision and computing time. Another Maximal Similarity based Region Merging (MSRM) segmentation [13] presents a more robust interactive method. According to MSRM, the scheme is divided into two stages. In the first stage, MSRM only merges the marker background regions. For each region B B M ∈ and its adjacent region i A that 1, 2 , , , { } i B B i i r A S S A = ∈ =  are merged into one region if 1, 2 ,. , ( , ) max ( , ), A j i B j k i i i A B A S A M ρ ρ = = ∉ (1) and the remained non-marker regions are merged in the second stage. For each non-marker region P N ∈ and its adjacent region i H that 1, 2 , , 1, 2 , , , { } , { } i H i P P i i p H j j p i H S S H S S = = ∈ = =   are merged into one region if 1, 2 ,. , ( , ) max ( , ), , H j i B i O j k i i i P H H S H M H M ρ ρ