Automated temporal tracking and segmentation of lymphoma on serial CT examinations

Abstract

Purpose: It is challenging to reproducibly measure and compare cancer lesions on numerous follow-up studies; the process is time-consuming and error-prone. In this paper, we show a method to automatically and reproducibly identify and segment abnormal lymph nodes in serial computed tomography (CT) exams. Methods: Our method leverages initial identification of enlarged (abnormal) lymph nodes in the baseline scan. We then identify an approximate region for the node in the follow-up scans using nonrigid image registration. The baseline scan is also used to locate regions of normal, non-nodal tissue surrounding the lymph node and to map them onto the follow-up scans, in order to reduce the search space to locate the lymph node on the follow-up scans. Adaptive region-growing and clustering algorithms are then used to obtain the final contours for segmentation. We applied our method to 24 distinct enlarged lymph nodes at multiple time points from 14 patients. The scan at the earlier time point was used as the baseline scan to be used in evaluating the follow-up scan, resulting in 70 total test cases (e.g., a series of scans obtained at 4 time points results in 3 test cases). For each of the 70 cases, a "reference standard" was obtained by manual segmentation by a radiologist. Assessment according to response evaluation criteria in solid tumors (RECIST) using our method agreed with RECIST assessments made using the reference standard segmentations in all test cases, and by calculating node overlap ratio and Hausdorff distance between the computer and radiologist-generated contours. Results: Compared to the reference standard, our method made the correct RECIST assessment for all 70 cases. The average overlap ratio was 80.7 6 9.7% s.d., and the average Hausdorff distance was 3.2 6 1.8 mm s.d. The concordance correlation between automated and manual segmentations was 0.978 (95% confidence interval 0.962, 0.984). The 100% agreement in our sample between our method and the standard with regard to RECIST classification suggests that the true disagreement rate is no more than 6%. Conclusions: Our automated lymph node segmentation method achieves excellent overall segmentation performance and provides equivalent RECIST assessment. It potentially will be useful to streamline and improve cancer lesion measurement and tracking and to improve assessment of cancer treatment response.

Imaging genomics in cancer research: limitations and promises

“…Some are based on the analysis of (often multiparametric) imaging signals in an unsupervised 83,84 or supervised way. 85 Oftentimes, anatomical statistical priors encode normal anatomy and hence find tumours as deviations from it.…”

Automated method for detection and segmentation of liver metastatic lesions in follow-up CT examinations

“…We next compare two algorithm steps of the presented methodology to the methodology described in Xu et al 14 For the next set of experiments, we used a randomly selected set of 59 test cases (out of the 127 lesion FU cases). One component is the alignment step, or registration.…”

“…The current work was motivated by Xu et al, 14 in which abnormal lymph nodes in serial CT exams are segmented using context from the baseline image, followed by an adaptive regiongrowing algorithm. We next compare two algorithm steps of the presented methodology to the methodology described in Xu et al 14 For the next set of experiments, we used a randomly selected set of 59 test cases (out of the 127 lesion FU cases).…”

“…14 An optimal threshold value t in the region-growing algorithm was chosen to maximize the compactness of the boundary, 14 defined as CðtÞ ¼ A∕P 2 , where A is the area and P is the perimeter, with two main conditions:…”

Mediastinal lymph node detection and station mapping on chest CT using spatial priors and random forest

“…Many works about lymph node segmentation in CT images have been developed. [33][34][35][36][37][38][39][40][41] In this work, using the centroids of lymph node candidates as seed points [ Fig. 8(a) a level-set-based curve evolution is applied [ Fig.…”