Evaluation of five image registration tools for abdominal CT: pitfalls and opportunities with soft anatomy

Abstract

Image registration has become an essential image processing technique to compare data across time and individuals. With the successes in volumetric brain registration, general-purpose software tools are beginning to be applied to abdominal computed tomography (CT) scans. Herein, we evaluate five current tools for registering clinically acquired abdominal CT scans. Twelve abdominal organs were labeled on a set of 20 atlases to enable assessment of correspondence. The 20 atlases were pairwise registered based on only intensity information with five registration tools (affine IRTK, FNIRT, Non-Rigid IRTK, NiftyReg, and ANTs). Following the brain literature, the Dice similarity coefficient (DSC), mean surface distance, and Hausdorff distance were calculated on the registered organs individually. However, interpretation was confounded due to a significant proportion of outliers. Examining the retrospectively selected top 1 and 5 atlases for each target revealed that there was a substantive performance difference between methods. To further our understanding, we constructed majority vote segmentation with the top 5 DSC values for each organ and target. The results illustrated a median improvement of 85% in DSC between the raw results and majority vote. These experiments show that some images may be well registered to some targets using the available software tools, but there is significant room for improvement and reveals the need for innovation and research in the field of registration in abdominal CTs. If image registration is to be used for local interpretation of abdominal CT, great care must be taken to account for outliers (e.g., atlas selection in statistical fusion).

Evaluation of body-wise and organ-wise registrations for abdominal organs

“…For uniformity, the dataset used to compare the two registration methods is the same as the dataset used to previously evaluate five different registration tools using the body-wise approach [5]. It comprises of 20 anonymous and randomly selected CT scans in NIFTI format from an ongoing colorectal cancer chemotherapy trial under Institutional Review Board (IRB) supervision.…”

“…0.6 × 0.6 × 3.0 mm ~ 1.0 × 1.0 × 5.0 mm) are found in the 20 datasets. Note this differs from [5] in that (1) the images were not cropped before registrations, and (2) the adrenal glands were divided into two separate labels. Image orientations were normalized in the NIFTI header and thirteen abdominal organs were considered as ROIs, namely spleen, right kidney, left kidney, gall bladder, esophagus, liver, stomach, aorta, inferior vena cava, portal and splenic vein, pancreas, right adrenal gland and left adrenal gland.…”

“…Currently, general-purpose registration tools initially designed for brain structures are applied to abdomen [3, 4]. In a previous study [5], we followed the framework of Klein et al [6] to evaluate four non-rigid registration tools including FNIRT [7], IRTK [8], ANTs [9], and NiftyReg [10] (all with default parameters) on their performances of abdominal CT registration regarding volumetric overlap and surface distance error. The results indicated that (1) all evaluated registration tools had a large portion of catastrophic registration failures that could undermine the further processing like the atlas-based segmentation, and (2) although better parameter selection may enhance the registration performances, new perspectives of addressing the challenges in abdomen were critical to improve the robustness of abdominal registrations.…”

“…This provides an opportunity to approach the abdominal registration problem in a discontinuous manner, which might be beneficial to overcome the distorting effects between the abdominal organs. In this study, we chose the registration tool with the best performances in [5], and compare its traditional registrations between the whole abdominal CT scans (body-wise with the registrations between organ-specific regions of interest (ROIs) localized by RF (organ-wise). Note this work was an extension of [5] on the same 20 datasets and studied in multi-atlas labeling and statistical fusion in [6].…”

“…In this study, we chose the registration tool with the best performances in [5], and compare its traditional registrations between the whole abdominal CT scans (body-wise with the registrations between organ-specific regions of interest (ROIs) localized by RF (organ-wise). Note this work was an extension of [5] on the same 20 datasets and studied in multi-atlas labeling and statistical fusion in [6]. Here here we focus on the evaluating the efficacy of using organ-wise registration.…”