Automatic liver contouring for radiotherapy treatment planning

Abstract

To develop automatic and efficient liver contouring software for planning 3D-CT and four-dimensional computed tomography (4D-CT) for application in clinical radiation therapy treatment planning systems.The algorithm comprises three steps for overcoming the challenge of similar intensities between the liver region and its surrounding tissues. First, the total variation model with the L1 norm (TV-L1), which has the characteristic of multi-scale decomposition and an edge-preserving property, is used for removing the surrounding muscles and tissues. Second, an improved level set model that contains both global and local energy functions is utilized to extract liver contour information sequentially. In the global energy function, the local correlation coefficient (LCC) is constructed based on the gray level co-occurrence matrix both of the initial liver region and the background region. The LCC can calculate the correlation of a pixel with the foreground and background regions, respectively. The LCC is combined with intensity distribution models to classify pixels during the evolutionary process of the level set based method. The obtained liver contour is used as the candidate liver region for the following step. In the third step, voxel-based texture characterization is employed for refining the liver region and obtaining the final liver contours.The proposed method was validated based on the planning CT images of a group of 25 patients undergoing radiation therapy treatment planning. These included ten lung cancer patients with normal appearing livers and ten patients with hepatocellular carcinoma or liver metastases. The method was also tested on abdominal 4D-CT images of a group of five patients with hepatocellular carcinoma or liver metastases. The false positive volume percentage, the false negative volume percentage, and the dice similarity coefficient between liver contours obtained by a developed algorithm and a current standard delineated by the expert group are on an average 2.15-2.57%, 2.96-3.23%, and 91.01-97.21% for the CT images with normal appearing livers, 2.28-3.62%, 3.15-4.33%, and 86.14-93.53% for the CT images with hepatocellular carcinoma or liver metastases, and 2.37-3.96%, 3.25-4.57%, and 82.23-89.44% for the 4D-CT images also with hepatocellular carcinoma or liver metastases, respectively.The proposed three-step method can achieve efficient automatic liver contouring for planning CT and 4D-CT images with follow-up treatment planning and should find widespread applications in future treatment planning systems.

Automation in intensity modulated radiotherapy treatment planning—a review of recent innovations

“…Automation of these areas is also being addressed but have not been covered in this review. [197][198][199][200][201][202][203][204] Real-time interactive planning is a new paradigm for IMRT planning that is not based on using traditional optimisation algorithms. Instead, the goal is to be able to give the treatment planner the ability to perform real-time and interactive manipulation of the isodose or DVH lines and ultra-fast (automated) reoptimisation and dose estimation to update the user on the impact of interactive modifications.…”

Parotid gland radiation dose‐xerostomia relationships based on actual delivered dose for nasopharyngeal carcinoma

“…We have researched the deformable registration algorithm over the last few years 24, 25. We also researched automatic contouring based on segmentation for radiotherapy treatment planning 26, 27, 28. We found that intensity‐based deformable registration algorithms are susceptible to distorting tissues because the presence of inconsistent intensities between CT and CBCT could cause significant registration errors.…”

Efficient Liver Segmentation from Computed Tomography Images Using Deep Learning

“…The details of different methods are given with pros and cons in the literature [ 3 , 4 ]. Various techniques have been suggested for the segmentation of a liver which are graph cut [ 5 , 6 ], level set [ 7 , 8 ], thresholding [ 9 ], and region growing [ 10 ]. Preventing under-segmentation and boundary leaks are the big issues in gray-level techniques where the intensity in different organs.…”