CROPro: a tool for automated cropping of prostate magnetic resonance images

Purpose To bypass manual data preprocessing and optimize deep learning performance, we developed and evaluated CROPro, a tool to standardize automated cropping of prostate magnetic resonance (MR) images. Approach CROPro enables automatic cropping of MR images regardless of patient health status, image size, prostate volume, or pixel spacing. CROPro can crop foreground pixels from a region of interest (e.g., prostate) with different image sizes, pixel spacing, and sampling strategies. Performance was evaluated in the context of clinically significant prostate cancer (csPCa) classification. Transfer learning was used to train five convolutional neural network (CNN) and five vision transformer (ViT) models using different combinations of cropped image sizes (64 × 64, 128 × 128, and 256 × 256 pixels2), pixel spacing (0.2 × 0.2, 0.3 × 0.3, 0.4 × 0.4, and 0.5 × 0.5 mm2), and sampling strategies (center, random, and stride cropping) over the prostate. T2-weighted MR images (N = 1475) from the online available PI-CAI challenge were used to train (N = 1033), validate (N = 221), and test (N = 221) all models. Results Among CNNs, SqueezeNet with stride cropping (image size: 128 × 128, pixel spacing: 0.2 × 0.2 mm2) achieved the best classification performance (0.678 ± 0.006). Among ViTs, ViT-H/14 with random cropping (image size: 64 × 64 and pixel spacing: 0.5 × 0.5 mm2) achieved the best performance (0.756 ± 0.009). Model performance depended on the cropped area, with optimal size generally larger with center cropping (∼40 cm2) than random/stride cropping (∼10 cm2). Conclusion We found that csPCa classification performance of CNNs and ViTs depends on the cropping settings. We demonstrated that CROPro is well suited to optimize these settings in a standardized manner, which could improve the overall performance of deep learning models.publishedVersio

Dynamics of Small-scale topographic heterogeneity in European sandy salt marshes

Heterogeneity can boost biodiversity, as well as increase the resilience of an ecosystem to changing environmental conditions; therefore, it is important to understand how topographic heterogeneity in ecosystems is formed. Sandy tidal marshes have a repetitive pattern of higher elevated hummocks surrounded by lower elevated depressions, representing topographic heterogeneity at the scale of a few square meters. The aims of this study were to determine when this topographic heterogeneity forms, how it is structured, and whether it persists during marsh development. The soil topography of marshes consists of coarse-grained sediment formed before marsh vegetation development, with an overlaying fine-grained sediment layer formed after initial marsh development. To gain insight into the formation of topographic heterogeneity, we studied the underlying soil topography of four European sandy marshes, where topographic heterogeneity at a scale of a few square meters was present. The differences in elevation between hummocks and depressions can either be caused by heterogeneity in the coarse-grained sediment or by heterogeneity in the top layer containing the fine-grained sediment. Our results showed that the largest percentage of elevational differences between hummocks and depressions could be attributed to heterogeneity in the underlying coarse-grained substratum. Therefore, we conclude that the patterns in all four marshes were primarily formed before marsh development, before fine-grained sediment was deposited on top of the coarse-grained sediment. However, a smaller percentage of the elevational difference between hummocks and depressions can also be explained by the presence of thicker fine-grained sediment layers on top of hummocks compared with depressions. This implies that marsh accretion rates were higher on hummocks compared with depressions. However, this result was limited to very early stages of marsh development, as marsh accretion rates estimated on marshes ranging between 15- and 120-years-old showed that depressions actually accreted sediments at a significantly faster rate than hummocks. Eventually, the patterns of heterogeneity stabilized and we found similar marsh accretion rates on hummocks and in depressions in the 120-year-old marsh, which resulted in the persistency of these topographic patterns

Artificial intelligence for prostate MRI: open datasets, available applications, and grand challenges

Artificial intelligence (AI) for prostate magnetic resonance imaging (MRI) is starting to play a clinical role for prostate cancer (PCa) patients. AI-assisted reading is feasible, allowing workflow reduction. A total of 3,369 multi-vendor prostate MRI cases are available in open datasets, acquired from 2003 to 2021 in Europe or USA at 3 T (n = 3,018; 89.6%) or 1.5 T (n = 296; 8.8%), 346 cases scanned with endorectal coil (10.3%), 3,023 (89.7%) with phased-array surface coils; 412 collected for anatomical segmentation tasks, 3,096 for PCa detection/classification; for 2,240 cases lesions delineation is available and 56 cases have matching histopathologic images; for 2,620 cases the PSA level is provided; the total size of all open datasets amounts to approximately 253 GB. Of note, quality of annotations provided per dataset highly differ and attention must be paid when using these datasets (e.g., data overlap). Seven grand challenges and commercial applications from eleven vendors are here considered. Few small studies provided prospective validation. More work is needed, in particular validation on large-scale multi-institutional, well-curated public datasets to test general applicability. Moreover, AI needs to be explored for clinical stages other than detection/characterization (e.g., follow-up, prognosis, interventions, and focal treatment).publishedVersio

Pelvic PET/MR attenuation correction in the image space using deep learning

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