In recent years, numerous effective multi-object tracking (MOT) methods are developed because of the wide range of applications. Existing performance evaluations of MOT methods usually separate the object tracking step from the object detection step by using the same fixed object detection results for comparisons. In this work, we perform a comprehensive quantitative study on the effects of object detection accuracy to the overall MOT performance, using the new large-scale University at Albany DE-Tection and tRACking (UA-DETRAC) benchmark dataset. The UA-DETRAC benchmark dataset consists of 100 challenging video sequences captured from real-world traffic scenes (over 140, 000 frames with rich annotations, including occlusion, weather, vehicle category, truncation, and vehicle bounding boxes) for object detection, object tracking and MOT system. We evaluate complete MOT systems constructed from combinations of state-of-the-art object detection and object tracking methods. Our analysis shows the complex effects of object detection accuracy on MOT system performance. Based on these observations, we propose new evaluation tools and metrics for MOT systems that consider both object detection and object tracking for comprehensive analysis.

Pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time-course data allows estimation of quantitative parameters such as K (trans) (rate constant for plasma/interstitium contrast agent transfer), v e (extravascular extracellular volume fraction), and v p (plasma volume fraction). A plethora of factors in DCE-MRI data acquisition and analysis can affect accuracy and precision of these parameters and, consequently, the utility of quantitative DCE-MRI for assessing therapy response. In this multicenter data analysis challenge, DCE-MRI data acquired at one center from 10 patients with breast cancer before and after the first cycle of neoadjuvant chemotherapy were shared and processed with 12 software tools based on the Tofts model (TM), extended TM, and Shutter-Speed model. Inputs of tumor region of interest definition, pre-contrast T1, and arterial input function were controlled to focus on the variations in parameter value and response prediction capability caused by differences in models and associated algorithms. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) values for K (trans) and v p being as high as 0.59 and 0.82, respectively. Parameter agreement improved when only algorithms based on the same model were compared, e.g., the K (trans) intraclass correlation coefficient increased to as high as 0.84. Agreement in parameter percentage change was much better than that in absolute parameter value, e.g., the pairwise concordance correlation coefficient improved from 0.047 (for K (trans)) to 0.92 (for K (trans) percentage change) in comparing two TM algorithms. Nearly all algorithms provided good to excellent (univariate logistic regression c-statistic value ranging from 0.8 to 1.0) early prediction of therapy response using the metrics of mean tumor K (trans) and k ep (=K (trans)/v e, intravasation rate constant) after the first therapy cycle and the corresponding percentage changes. The results suggest that the interalgorithm parameter variations are largely systematic, which are not likely to significantly affect the utility of DCE-MRI for assessment of therapy response.

Accurate pharmacokinetic (PK) modeling of Dynamic Contrast Enhanced MRI (DCE-MRI) in prostate cancer requires knowledge of the concentration time course of the contrast agent in the feeding vasculature, the so-called arterial input function (AIF). The purpose of this study was to compare AIF choice in differentiating peripheral zone prostate cancer (PCa) from non-neoplastic prostatic tissue (NNPT), using PK analysis of high temporal resolution prostate DCE-MRI data and whole-mount pathology (WMP) validation. This prospective study was performed in 30 patients who underwent multiparametric endorectal prostate MRI at 3.0T and WMP validation. PCa foci were annotated on WMP slides and MR images using 3D Slicer. Foci ≥0.5cm3 were contoured as tumor regions of interest (TROIs) on subtraction DCE (early-arterial - pre-contrast) images. PK analyses of TROI and NNPT data were performed using automatic AIF (aAIF) and model AIF (mAIF) methods. A paired t-test compared mean and 90th percentile (p90) PK parameters obtained with the two AIF approaches. ROC analysis determined diagnostic accuracy (DA) of PK parameters. Logistic regression determined correlation between PK parameters and histopathology. Mean TROI and NNPT PK parameters were higher using aAIF vs. mAIF (p<0.05). There was no significant difference in DA between AIF methods: highest for p90 Ktrans (aAIF differences in the area under the ROC curve (Az) =0.827; mAIF Az=0.93). Tumor cell density correlated with aAIF Ktrans (p=0.03). Our results indicate that DCE-MRI using both AIF methods is excellent in discriminating PCa from NNPT. If quantitative DCE-MRI is to be used as a biomarker in PCa, the same AIF method should be used consistently throughout the study.

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