Effect of latent space distribution on the segmentation of images with multiple annotations

We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution that can better approximate the uncertainty in the reference segmentations. We study the effect the choice of latent space distribution has on capturing the variation in the reference segmentations for lung tumors and white matter hyperintensities in the brain. We show that the choice of distribution affects the sample diversity of the predictions and their overlap with respect to the reference segmentations. We have made our implementation available at https://github.com/ishaanb92/GeneralizedProbabilisticUNetComment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2023:005. arXiv admin note: text overlap with arXiv:2207.1287

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.Comment: Submitted for publication. Comments welcome by email to first autho

Hippocampal T2 hyperintensities on 7Tesla MRI

AbstractHippocampal focal T2 hyperintensities (HT2Hs), also referred to as hippocampal sulcal cavities, are a common finding on Magnetic Resonance (MR) images. There is uncertainty about their etiology and clinical significance. In this study we aimed to describe these HT2Hs in more detail using high resolution 7Tesla MR imaging, addressing 1) the MR signal characteristics of HT2Hs, 2) their occurrence frequency, 3) their location within the hippocampus, and 4) their relation with age. We also performed an explorative post-mortem study to examine the histology of HT2Hs.Fifty-eight persons without a history of invalidating neurological or psychiatric disease (mean age 64±8years; range 43–78years), recruited through their general practitioners, were included in this study. They all underwent 7Tesla MRI, including a T1, T2, and FLAIR image. MR signal characteristics of the HT2Hs were assessed on these images by two raters. Also, the location and number of the HT2Hs were assessed. In addition, four formalin-fixed brain slices from two subjects were scanned overnight. HT2Hs identified in these slices were subjected to histopathological analysis.HT2Hs were present in 97% of the subjects (median number per person 10; range 0–20). All HT2Hs detected on the T2 sequence were hypointense on T1 weighted images. Of all HT2Hs, 94% was hypointense and 6% hyperintense on FLAIR. FLAIR hypointense HT2Hs were all located in the vestigial sulcus of the hippocampus, FLAIR hyperintense HT2Hs in the hippocampal sulcus or the gray matter. Post-mortem MRI and histopathological analysis suggested that the hypointense HT2Hs on FLAIR were cavities filled with cerebrospinal fluid. A hyperintense HT2H on FLAIR proved to be a microinfarct upon microscopy.In conclusion, hippocampal T2Hs are extremely common and unrelated to age. They can be divided into two types (hypo- and hyperintense on FLAIR), probably with different etiology

The Impact of Strategic White Matter Hyperintensity Lesion Location on Language

Objective: The impact of white matter hyperintensities (WMH) on language possibly depends on lesion location through disturbance of strategic white matter tracts. We examined the impact of WMH location on language in elderly Asians. Design: Cross-sectional. Setting: Population-based. Participants: Eight-hundred nineteen residents of Singapore, ages (≥65 years). Measurements: Clinical, cognitive and 3T magnetic resonance imaging assessments were performed on all participants. Language was assessed using the Modified Boston Naming Test (MBNT) and Verbal Fluency (VF). Hypothesis-free region-of-interest-based (ROI) analyses based on major white matter tracts were used to determine the association between WMH location and language. Conditional dependencies between the regional WMH volumes and language were examined using Bayesian-network analysis. Results: ROI-based analyses showed that WMH located within the anterior thalamic radiation (mean difference: −0.12, 95% confidence interval [CI]: −0.22; −0.02, p = 0.019) and uncinate fasciculus (mean difference: −0.09, 95% CI: −0.18; −0.01, p = 0.022) in the left hemisphere were significantly associated with worse VF but did not survive multiple testing. Conversely, WMH volume in the left cingulum of cingulate gyrus was significantly associated with MBNT performance (mean difference: −0.09, 95% CI: −0.17; −0.02, p = 0.016). Bayesian-network analyses confirmed the left cingulum of cingulate gyrus as a direct determinant of MBNT performance. Conclusion: Our findings identify the left cingulum of cingulate gyrus as a strategic white matter tract for MBNT, suggesting that language – is sensitive to subcortical ischemic damage. Future studies on the role of sporadic ischemic lesions and vascular cognitive impairment should not only focus on total WMH volume but should also take WMH lesion location into account when addressing language

Zooming in on cerebral small vessel function in small vessel diseases with 7T MRI: Rationale and design of the “ZOOM@SVDs” study

Background: Cerebral small vessel diseases (SVDs) are a major cause of stroke and dementia. Yet, specific treatment strategies are lacking in part because of a limited understanding of the underlying disease processes. There is therefore an urgent need to study SVDs at their core, the small vessels themselves. Objective: This paper presents the rationale and design of the ZOOM@SVDs study, which aims to establish measures of cerebral small vessel dysfunction on 7T MRI as novel disease markers of SVDs. Methods: ZOOM@SVDs is a prospective observational cohort study with two years follow-up. ZOOM@SVDs recruits participants with Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL, N = 20), sporadic SVDs (N = 60), and healthy controls (N = 40). Participants undergo 7T brain MRI to assess different aspects of small vessel function including small vessel reactivity, cerebral perforating artery flow, and pulsatility. Extensive work-up at baseline and follow-up further includes clinical and neuropsychological assessment as well as 3T brain MRI to assess conventional SVD imaging markers. Measures of small vessel dysfunction are compared between patients and controls, and related to the severity of clinical and conventional MRI manifestations of SVDs. Discussion: ZOOM@SVDs will deliver novel markers of cerebral small vessel function in patients with monogenic and sporadic forms of SVDs, and establish their relation with disease burden and progression. These small vessel markers can support etiological studies in SVDs and may serve as surrogate outcome measures in future clinical trials to show target engagement of drugs directed at the small vessels