Tracking Metastatic Brain Tumors in Longitudinal Scans via Joint Image Registration and Labeling

Abstract

The treatment of metastatic brain tumors with stereotactic radiosurgery requires that the clinician first locate the tumors and measure their volumes. Thoroughly searching a patient scan for brain tumors and delineating the lesions can be a long and difficult task when done manually and is also prone to human error. In this paper, we present an automated method for detecting changes in brain tumor lesions over longitudinal scans to aide the clinician’s task of determining tumor volumes. Our approach jointly registers the current image with a previous scan while estimating changes in intensity correspondences due to tumor growth or regression. We combine the label map with correspondence changes with tumor segmentations from a previous scan to estimate the metastases in the new image. Alignment and tumor tracking results show promise on 28 registrations using real patient data.

Detection of Volume-Changing Metastatic Brain Tumors on Longitudinal MRI Using a Semiautomated Algorithm Based on the Jacobian Operator Field

“…At present, the literature is limited in studies concerning the follow-up and volume-change detection of MBTs. Chitphakdithai et al 15 proposed a method for tracking MBTs, relying on a 4-level label map to denote the intensity-correspondence relation between baseline and follow-up images. Although authors reported a sensitivity of 92% in detecting ⌬MBTs, this was verified on only a limited dataset comprising 3 patients.…”

“…7 Moreover, the inherent limitations of viewing scans section by section, changes in head position from one scan to another, and user subjectivity result in the potential for increased inter-and intraobserver variability in both detection and volume assessment, especially with small MBTs or subtle volume changes. 8 Although several studies have investigated the efficiency of computer-aided detection techniques in MBTs on a single MR scan, [9][10][11][12][13][14] the literature is limited in studies evaluating the efficacy of computer algorithms in the follow-up of MBTs 15 and detection of volume-changing MBTs (⌬MBTs) as an indicator of treatment response. Tracking volumetric changes is of high clinical value, and implementing computer-aided techniques in the follow-up of MBTs can improve diagnostic accuracy and efficiency [16][17][18][19] and complement current single-scan detection algorithms.…”

A web‐based brain metastases segmentation and labeling platform for stereotactic radiosurgery

“…Recently, more BMs auto‐detection and segmentation algorithms are developed 24–29 . For the algorithms focusing on the BMs auto‐detection task, 28,29 manual input to contour the BMs is still needed after the auto‐detection.…”

Semiautomatic segmentation and follow-up of multicomponent low-grade tumors in longitudinal brain MRI studies

“…Quantification of longitudinal changes in brain MRI scans has gained much attention in recent years. [15][16][17][18][19][20][21][22][23][24] Some of these methods register follow-up MR scans and analyze the changes between the scans. Patriarche and Erickson 22 present a graylevel based change detection.…”

“…Angelini et al 15 address the nonlinear contrast change between the two data sets with normalization via midway histogram equalization. Chitphakdithai et al 16 simultaneously estimate the registration parameters and label the changes between two consecutive brain scans to track metastatic brain tumors. Pohl et al 23 present a pipeline method to segment a tumor in a set of longitudinal scans based on user guided segmentation of the first scan.…”