Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or protocols, which in turn can have a large influence on algorithm accuracy. The Prostate MR Image Segmentation (PROMISE12) challenge was setup to allow a fair and meaningful comparison of segmentation methods on the basis of performance and robustness. In this work we will discuss the initial results of the online PROMISE12 challenge, and the results obtained in the live challenge workshop hosted by the MICCAI2012 conference. In the challenge, 100 prostate MR cases from 4 different centers were included, with differences in scanner manufacturer, field strength and protocol. A total of 11 teams from academic research groups and industry participated. Algorithms showed a wide variety in methods and implementation, including active appearance models, atlas registration and level sets. Evaluation was performed using boundary and volume based metrics which were combined into a single score relating the metrics to human expert performance. The winners of the challenge where the algorithms by teams Imorphics and ScrAutoProstate, with scores of 85.72 and 84.29 overall. Both algorithms where significantly better than all other algorithms in the challenge (p < 0.05) and had an efficient implementation with a run time of 8 minutes and 3 second per case respectively. Overall, active appearance model based approaches seemed to outperform other approaches like multi-atlas registration, both on accuracy and computation time. Although average algorithm performance was good to excellent and the Imorphics algorithm outperformed the second observer on average, we showed that algorithm combination might lead to further improvement, indicating that optimal performance for prostate segmentation is not yet obtained. All results are available online at http://promise12.grand-challenge.org/.

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NEURORADIOLOGYI dentification of the presence and extent of infarcted brain tissue at baseline plays a crucial role in the management of acute ischemic stroke (AIS). Patients with extensive infarction at baseline are unlikely to benefit from thrombolysis or thrombectomy procedures (1-4). Non-contrastenhanced CT is the most widely used imaging modality to assess the extent of infarction in patients with acute stroke (depicted as hypoattenuation). The Alberta Stroke Program Early CT Score (ASPECTS) offers a semiquantitative approach to subjectively assess infarction extent on non-contrast-enhanced CT images (1,2,5). ASPECTS assessment with this method, however, requires expertise, something not readily available in smaller hospitals or during off hours (6). Moreover, because it is semiquantitative, ASPECTS offers only an approximation of the total extent of early ischemic changes. For example, a patient with an ASPECTS of 6 could have a large infarct if most of the cortical regions are involved; another patient with the same ASPECTS of 6 could have a relatively small infarct if only subcortical regions are involved.Quantitative estimation of infarction with non-contrastenhanced CT is challenging because the density and texture variations in the involved brain regions are subtle and can be confounded by normal physiologic changes or old lesions (7,8). Low signal-to-noise ratio, thick slices, and low contrast in images of brain tissue make most traditional image-based segmentation approaches difficult (eg, thresholding-derived, region-based, edge-based, or model-based methods) (7). Recent advances in image processing that use deep learningor machine learning (ML)-based algorithms have potential

BACKGROUND AND PURPOSE: Alberta Stroke Program Early CT Score (ASPECTS) was devised as a systematic method to assess the extent of early ischemic change on noncontrast CT (NCCT) in patients with acute ischemic stroke (AIS). Our aim was to automate ASPECTS to objectively score NCCT of AIS patients. MATERIALS AND METHODS: We collected NCCT images with a 5-mm thickness of 257 patients with acute ischemic stroke (Ͻ8 hours from onset to scans) followed by a diffusion-weighted imaging acquisition within 1 hour. Expert ASPECTS readings on DWI were used as ground truth. Texture features were extracted from each ASPECTS region of the 157 training patient images to train a random forest classifier. The unseen 100 testing patient images were used to evaluate the performance of the trained classifier. Statistical analyses on the total ASPECTS and region-level ASPECTS were conducted. RESULTS: For the total ASPECTS of the unseen 100 patients, the intraclass correlation coefficient between the automated ASPECTS method and DWI ASPECTS scores of expert readings was 0.76 (95% confidence interval, 0.67-0.83) and the mean ASPECTS difference in the Bland-Altman plot was 0.3 (limits of agreement, Ϫ3.3, 2.6). Individual ASPECTS region-level analysis showed that our method yielded ϭ 0.60, sensitivity of 66.2%, specificity of 91.8%, and area under curve of 0.79 for 100 ϫ 10 ASPECTS regions. Additionally, when ASPECTS was dichotomized (Ͼ4 and Յ4), ϭ 0.78, sensitivity of 97.8%, specificity of 80%, and area under the curve of 0.89 were generated between the proposed method and expert readings on DWI. CONCLUSIONS: The proposed automated ASPECTS scoring approach shows reasonable ability to determine ASPECTS on NCCT images in patients presenting with acute ischemic stroke.

Background and Purpose— Computed tomographic perfusion (CTP) thresholds associated with follow-up brain infarction may differ by time from symptom onset to imaging and reperfusion. We confirm CTP thresholds over time to imaging and reperfusion in patients with acute ischemic stroke from the HERMES collaboration (Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke Trials) data. Methods— Patients with occlusion on CT angiography were acutely imaged with CTP. Noncontrast CT and magnetic resonance-diffusion weighted imaging at 24 to 48 hours defined follow-up infarction. Reperfusion was assessed on conventional angiogram. Tmax, cerebral blood flow (CBF), and cerebral blood volume maps were derived from delay-insensitive CTP postprocessing. These parameters were analyzed using receiver operator characteristics to derive optimal thresholds based on time from stroke onset-to-CTP or to reperfusion. ANOVA and linear regression were used to test whether the derived CTP thresholds were different by time. Results— One hundred thirty-seven patients were included. Tmax thresholds of >15.7 s and >15.8 s and absolute CBF thresholds of <8.9 and <7.5 mL·min −1 ·100 g −1 for gray matter and white matter respectively were associated with infarct if reperfusion was achieved <90 minutes from CTP with stroke onset-to-CTP <180 minutes. The discriminative ability of cerebral blood volume was modest. There were no statistically significant relationships between stroke onset-to-CTP time and Tmax, CBF, and cerebral blood volume thresholds (all P >0.05). A statistically significant relationship was observed between CTP-to-reperfusion time and the optimal thresholds for Tmax ( P <0.001) and CBF ( P <0.001). Similar but more modest relationship was noted for onset-to-reperfusion time and optimal thresholds for CBF ( P ≤0.01). Conclusions— CTP thresholds based on stroke onset and imaging time and taking into account time needed for reperfusion may improve infarct prediction in patients with acute ischemic stroke.