Deep active learning for suggestive segmentation of biomedical image stacks via optimisation of Dice scores and traced boundary length.

Manual segmentation of stacks of 2D biomedical images (e.g., histology) is a time-consuming task which can be sped up with semi-automated techniques. In this article, we present a suggestive deep active learning framework that seeks to minimise the annotation effort required to achieve a certain level of accuracy when labelling such a stack. The framework suggests, at every iteration, a specific region of interest (ROI) in one of the images for manual delineation. Using a deep segmentation neural network and a mixed cross-entropy loss function, we propose a principled strategy to estimate class probabilities for the whole stack, conditioned on heterogeneous partial segmentations of the 2D images, as well as on weak supervision in the form of image indices that bound each ROI. Using the estimated probabilities, we propose a novel active learning criterion based on predictions for the estimated segmentation performance and delineation effort, measured with average Dice scores and total delineated boundary length, respectively, rather than common surrogates such as entropy. The query strategy suggests the ROI that is expected to maximise the ratio between performance and effort, while considering the adjacency of structures that may have already been labelled - which decrease the length of the boundary to trace. We provide quantitative results on synthetically deformed MRI scans and real histological data, showing that our framework can reduce labelling effort by up to 60-70% without compromising accuracy.

Monitoring tissue engineered constructs and protocols with laboratory-based x-ray phase contrast tomography.

Tissue engineering (TE) aims to generate bioengineered constructs which can offer a surgical treatment for many conditions involving tissue or organ loss. Construct generation must be guided by suitable assessment tools. However, most current tools (e.g. histology) are destructive, which restricts evaluation to a single-2D anatomical plane, and has no potential for assessing constructs prior to or following their implantation. An alternative can be provided by laboratory-based x-ray phase contrast computed tomography (PC-CT), which enables the extraction of 3D density maps of an organ's anatomy. In this work, we developed a semi-automated image processing pipeline dedicated to the analysis of PC-CT slices of oesophageal constructs. Visual and quantitative (density and morphological) information is extracted on a volumetric basis, enabling a comprehensive evaluation of the regenerated constructs. We believe the presented tools can enable the successful regeneration of patient-specific oesophagus, and bring comparable benefit to a wide range of TE applications. STATEMENT OF SIGNIFICANCE: Phase contrast computed tomography (PC-CT) is an imaging modality which generates high resolution volumetric density maps of biological tissue. In this work, we demonstrate the use of PC-CT as a new tool for guiding the progression of an oesophageal tissue engineering (TE) protocol. Specifically, we developed a semi-automated image-processing pipeline which analyses the oesophageal PC-CT slices, extracting visual and quantitative (density and morphological) information. This information was proven key for performing a comprehensive evaluation of the regenerated constructs, and cannot be obtained through existing assessment tools primarily due to their destructive nature (e.g. histology). This work paves the way for using PC-CT in a wide range of TE applications which can be pivotal for unlocking the potential of this field.

Perivascular space dilation is associated with vascular amyloid-ß accumulation in the overlying cortex.

Perivascular spaces (PVS) are compartments surrounding cerebral blood vessels that become visible on MRI when enlarged. Enlarged PVS (EPVS) are commonly seen in patients with cerebral small vessel disease (CSVD) and have been suggested to reflect dysfunctional perivascular clearance of soluble waste products from the brain. In this study, we investigated histopathological correlates of EPVS and how they relate to vascular amyloid-ß (Aß) in cerebral amyloid angiopathy (CAA), a form of CSVD that commonly co-exists with Alzheimer's disease (AD) pathology. We used ex vivo MRI, semi-automatic segmentation and validated deep-learning-based models to quantify EPVS and associated histopathological abnormalities. Severity of MRI-visible PVS during life was significantly associated with severity of MRI-visible PVS on ex vivo MRI in formalin fixed intact hemispheres and corresponded with PVS enlargement on histopathology in the same areas. EPVS were located mainly around the white matter portion of perforating cortical arterioles and their burden was associated with CAA severity in the overlying cortex. Furthermore, we observed markedly reduced smooth muscle cells and increased vascular Aß accumulation, extending into the WM, in individually affected vessels with an EPVS. Overall, these findings are consistent with the notion that EPVS reflect impaired outward flow along arterioles and have implications for our understanding of perivascular clearance mechanisms, which play an important role in the pathophysiology of CAA and AD.

Quantification of volumetric morphometry and optical property in the cortex of human cerebellum at micrometer resolution.

The surface of the human cerebellar cortex is much more tightly folded than the cerebral cortex. Volumetric analysis of cerebellar morphometry in magnetic resonance imaging studies suffers from insufficient resolution, and therefore has had limited impact on disease assessment. Automatic serial polarization-sensitive optical coherence tomography (as-PSOCT) is an emerging technique that offers the advantages of microscopic resolution and volumetric reconstruction of large-scale samples. In this study, we reconstructed multiple cubic centimeters of ex vivo human cerebellum tissue using as-PSOCT. The morphometric and optical properties of the cerebellar cortex across five subjects were quantified. While the molecular and granular layers exhibited similar mean thickness in the five subjects, the thickness varied greatly in the granular layer within subjects. Layer-specific optical property remained homogenous within individual subjects but showed higher cross-subject variability than layer thickness. High-resolution volumetric morphometry and optical property maps of human cerebellar cortex revealed by as-PSOCT have great potential to advance our understanding of cerebellar function and diseases.