Emerging Trends in Image Processing Computer

resected liver region usingAutomatic estimation of aa tumor domination ratio

23 M. Hariyama 1 , M. Shimoda 2 1 Graduate

School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan 2 Second

Department of Surgery, Dokkyo Medical University, Tochigi, Japan

1 INTRODUCTION 3D simulation, recently, plays an important role in surgical planning for hepatectomy since the liver has a complex structure, that is, some different vessels exist complexly as shown in Figure 1. The estimation of regions perfused by the portal vein is especially one important task in anatomical hepatectomy, since the hepatocellular carcinoma (HCC), cancerous liver tumor, tends to metastasize via the portal vein [1–3]. Figure 2 explains how HCC metastasizes through the portal vein and what a perfused region is. The tumor is fed by the nearest parts of the portal vein, and HCC metastasizes in the downstream direction through the portal vein. Therefore, the cancerrelated regions can be estimated based on the perfused region. Overestimating the perfused region tends to prevent metastasizing and reduce recurrence of HCC since a large region including the tumor is resected. However, it increases the possibility of the postoperative liver failure. Therefore, the volume of the perfused region should be minimized while including all the perfused regions of the portal vein feeding the tumor. Some practical software programs for 3D simulation and preoperative planning have been developed like Hepavision (MeVis Medical Solutions AG, Bremen, Germany) [4], OVA (Hitachi, Tokyo, Japan) [5], and Synapse VINCENT (Fujifilm Medical, Tokyo, Japan) [6]. In preoperative planning, surgeons search for the combination of the cut-points on the portal vein in a trial -and-error way until the tumor is covered by the perfused regions of the portal -vein cut-points. However, it is difficult and time-consuming for surgeons to find optimal or near-optimal combination since the portals vein has complex structure, that is, it has many branches. As a result, surgeons tend to select simple combinations, and this leads to overestimation of the perfused region.

370 CHAPTER23 Automatic estimation of a resected liver region To solve this problem, the method to compute the optimal perfused regions has been developed, where the volume is minimized while including all the perfused region of the tumor-related portal vein [7]. In order to find the tumor-related part Emerging Trends in Image Processing, Computer Vision and Pattern Recognition

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http://dx.doi.org/10.1016/ B978-0 -12-8 02045-6.000 23-5 © 2015 Elsevier Inc. All rights reserved.

FIGURE 1

Tumor

Dow nstream

Portal vein

P Dow nstream

FIGURE 2

of the portal vein accurately, the tumor domination ratio (TDR) is defined which reflects how much volume of the tumor a portal -vein point feeds. The TDR allows to pick up all the tumorrelated points of the portal vein, and the total of the regions perfus ed by them is determined to be the resected region. This chapter extends the work [7] by adding explanations, evaluations, and a method considering a more practical condition. The rest of this chapter is organized as follows. In Section 2, the basic method to find the ideal resected region is explained where practical conditions used in the real surgeries are not considered. In Section 3, its extension is explained where practical conditions such as the

371 branch points and the radiuses of the vessels are cons idered. Section 4 describes more practical estimation considering hepatic veins as well as portal veins. Section 5 is conclusion.

2 Estimating an ideal resected region using the TDR

2 ESTIMATING AN IDEAL RESECTED REGION USING THE TDR Perfused regions are typically estimated by using a Voronoi diagram [3]. Figure 3 shows how to compute perfused regions using Voronoi diagram. A Voronoi diagram is a way of dividing space into a number of regions as shown in Figure 3(a). A set of points (called seeds) is specified beforehand and for each seed there will be a corresponding region consisting of all points closer to that seed than to any other. The corresponding regions are called Voronoi cells. In estimating perfused regions, a seed and Voronoi cell in a Voronoi diagram correspond to a point on a portal vein and a region perfumed by the portal point, respectively, as shown in Figure 3(b). Note that, in Figure 3(b), portal-vein points (seeds) exist continuously, whereas seeds in the original Voronoi diagram are located apart from each other. As a result, the liver region is divided into small regions of the same number as the portal -vein points. The TDR of a portal-vein point P is defined as Vol ume of the region perfused of P i n the tumor TDR¼

Vol ume of the tumor "100 ½%$ (1)

where the denominator and enumerator are illustrated in Figure 3(c). The TDR reflects how much the portal-vein point feed the tumor and is also affected by the tumor. Given a tumor location, let us compute an ideal resected region by using the TDR, where the ideal resected region means a minimum region including all subregions perfused by tumor-related portal points. The ideal resected region is estimated by following steps (Figure 4). Step 1: Among the portal-vein points with TDR larger than 0 on a branch, select the point closest to the main stem as a cut-point of the branch. For example, in Figure 4(a), Pi (i¼1, 2,%%%, 6) are the portal -vein points with TDR larger than 0; P 1 and P4 are selected as cutpoints on branches B1 and B2 , respectively.

372 CHAPTER23 Automatic estimation of a resected liver region FIGURE 3

Computing perfused regions using Voronoi diagram. Liver surface

Liver surface

Cut-point P1 P2

P3

Downstream Branch B1

P1 Branch B1

Tumor To the main stem

P4

P5

Cut-point

Branch B2 P6

(a)

P5

Branch B2

Downstream

(b) 1

P1

Total resected region

P 2P3 Tumor

P4

Tumor P5

P6

Liver surface

(c)

Liver surface

(d)

Resected regionrelated to branch B

Resected regionrelated to branch B 1

FIGURE 4

Computing ideal resected region.

Step 2: For each branch, the downstream part of the cut-point is computed by region growing (Figure 4(b)). For all the portal-vein points of these downstream parts, the perfused regions are computed as shown in Figure 4(c). Step 3: The ideal resected region is given by a union of all the perfused regions of all the points on downstream parts as shown in Figure 4(d).

373 Let us compare the proposed method with the manual approach on the conventional 3D simulation software program (Synapse VINCENT ver.2). Figure 5 shows the sample date used for evaluation. The samples 1 and 2 are the cases where the small tumor exists near the liver surface; the sample 3 is the case where the large tumor exists near the main stem of the portal vein. The liver, vessels, and a tumor are extracted in advance by the other software programs. Tables 1–3 summarize the results for samples 1–3, respectively. From Tables 1 and 2, the proposed optimization method is very useful compared to the manual approach, that is, the volume of the estimated resected region by the proposed method is much smaller than that of the manual approach. This is because the degree of freedom of cut-point selection is high and the effect of optimization is large for the case where the small tumor exists near

2 Estimating an ideal resected region using the TDR Hepatic vein Tumor

Portal vein

(a )

Sample 1

( b)

Sample 2

(c )

Sample 3

FIGURE 5

Samples for evaluation.

Table 1 Comparison Results for Sample 1 Sample 1

Manual Approach by a Surgeon

Proposed

Resected volume (cc)

192

66

The number of cut-points

1

2

Results

Resected region

Resected region

Table 2 Comparison Results for Sample 2 Sample 2

Manual Approach by a Surgeon

Proposed

Resected volume(cc)

325

42

The number of cut-points

2

4

Results

Resected region

Resected region

Table 3 Comparison Results for Sample 3 Sample 3

Manual Approach by a Surgeon

Proposed

Resected volume (cc)

550

476

The number of cut-points

5

10

374 CHAPTER23 Automatic estimation of a resected liver region Results

Resected region

Resected region

the liver surface. On the other hand, from Table 3, the optimization is not very effective when the tumor exists near the main stem of the portal vein since the degree of freedom of cutpoint selection is low. Let us evaluate the computation time. The computing platform that we use is a PC with a Core-i7 CPU and a 24 GB memory. The operating system and programming language are Windows 7 and Java 1.6, respectively. The program simply designed for single-thread execution. The main reason why we use Java as a programming language is that the Java based image-processing platform called ImageJ [8] is easy to use for medical image processing. ImageJ has a lot of plugins like a DICOM reader, basic 2D/3D image processing and visualization, and can be extended easily by adding user -defined plugins. Moreover, ImageJ runs on multiplatforms like Microsoft Windows, Linux, and MacOS since it is based on Java. Table 4 shows the computation times for samples 1, 2, and 3 and their details. Note that vessel extraction, liver extraction, and tumor extraction are done in advanc e as explained above, and that their computation times are not included. The time for “computing perfused regions” is the one for Step 1 and Step 2; the time for “computing resected regions ” is the one for Step 3. The computation times vary depending on the volumes of the perfused regions and the cut region. The larger the volumes are, the longer the computation time is. The computation time for any sample is