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PRCIS: The lamina cribrosa pores of high-tension glaucoma subjects appear to take a more tortuous pathway than the LC pores of non-glaucomatous subjects. PURPOSE: To compare the lamina cribrosa pore microarchitecture in high-tension glaucoma (HTG), normal-tension glaucoma (NTG) and in non-glaucomatous (NG) subjects, by reconstructions of the lamina cribrosa made from tomographic images. PATIENTS AND METHODS: SD-OCT images of 52 eyes (18 NG, 18 HTG, 16 NTG) of 29 patients were analyzed. Pores were traced using segmentation software. Pore length, tortuosity and verticality were the three quantitative parameters compared between the three groups. Correlation analyses were performed to determine the effects of covariates on the three quantitative parameters. RESULTS: Pore tortuosity in HTG (1.419 +/- 0.093) was significantly higher (P=0.011) than in NG (1,347 +/- 0,034) but did not differ from that of NTG eyes (P=0.251). In addition, NTG had significantly shorter pores (P=0.005) than NG. No difference in pore tortuosity or verticality was found between NG and NTG (P=0.587 and P=0.120 respectively). Pore verticality and length in HTG eyes did not significantly differ from that of NG eyes (P=0.049 and P=0.033 respectively) and NTG eyes (P=0.827 and P=0.968 respectively). All of the quantitative parameters measured were not correlated with age, but were associated with glaucoma severity (VFI, MD, RNFL, GCC), except for pore verticality which was not correlated with RNFL. CONCLUSION: The LC pores of HTG subjects appear to be more tortuous than the pores of NG subjects and the pores of NTG patients are shorter than those of NG subjects. Changes in pore parameters appear to be associated with severity of the glaucomatous optic neuropathy.

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Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscular Lesions DetectiOn and Segmentation (Where is VALDO?) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1-EPVS, 9 for Task 2-Microbleeds and 6 for Task 3-Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1-EPVS and Task 2-Microbleeds and not practically useful results yet for Task 3-Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level.

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We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.

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Preterm birth is a multifactorial condition associated with increased morbidity and mortality. Diffuse excessive high signal intensity (DEHSI) has been recently described on T2-weighted MR sequences in this population and thought to be associated with neuropathologies. To date, no robust and reproducible method to assess the presence of white matter hyperintensities has been developed, perhaps explaining the current controversy over their prognostic value. The aim of this paper is to propose a new semi-automated framework to detect DEHSI on neonatal brain MR images having a particular pattern due to the physiological lack of complete myelination of the white matter. A novel method for semi- automatic segmentation of neonatal brain structures and DEHSI, based on mathematical morphology and on max-tree representations of the images is thus described. It is a mandatory first step to identify and clinically assess homogeneous cohorts of neonates for DEHSI and/or volume of any other segmented structures. Implemented in a user-friendly interface, the method makes it straightforward to select relevant markers of structures to be segmented, and if needed, apply eventually manual corrections. This method responds to the increasing need for providing medical experts with semi-automatic tools for image analysis, and overcomes the limitations of visual analysis alone, prone to subjectivity and variability. Experimental results demonstrate that the method is accurate, with excellent reproducibility and with very few manual corrections needed. Although the method was intended initially for images acquired at 1.5T, which corresponds to the usual clinical practice, preliminary results on images acquired at 3T suggest that the proposed approach can be generalized.

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