Late lesion growth following endovascular therapy: is 24 hours too early to assess acute infarct size including the effects of secondary injury?

Introduction Stroke lesion volume on MRI or CT provides objective evidence of tissue injury as a consequence of ischemic stroke. Measurement of "final" lesion volume at 24hr following endovascular therapy (post-EVT) has been used in multiple studies as a surrogate for clinical outcome. However, despite successful recanalization, a significant proportion of patients do not experience favorable clinical outcome. The goals of this study were to quantify lesion growth during the first week after treatment, identify early predictors, and explore the association with clinical outcome. Methods This is a prospective study of stroke patients at two centers who met the following criteria: i) anterior large vessel occlusion (LVO) acute ischemic stroke, ii) attempted EVT, and iii) had 3T MRI post-EVT at 24hr and 5-day. We defined "Early" and "Late" lesion growth as ≥10mL lesion growth between baseline and 24hr DWI, and between 24hr DWI and 5-day FLAIR, respectively. Complete reperfusion was defined as >90% reduction of the volume of tissue with perfusion delay (Tmax>6sec) between pre-EVT and 24hr post-EVT. Favorable clinical outcome was defined as modified Rankin scale (mRS) of 0-2 at 30 or 90 days. Results One hundred twelve patients met study criteria with median age 67 years, 56% female, median admit NIHSS 19, 54% received IV or IA thrombolysis, 66% with M1 occlusion, and median baseline DWI volume 21.2mL. Successful recanalization was achieved in 87% and 68% had complete reperfusion, with an overall favorable clinical outcome rate of 53%. Nearly two thirds (65%) of the patients did not have Late lesion growth with a median volume change of -0.3mL between 24hr and 5-days and an associated high rate of favorable clinical outcome (64%). However, ~1/3 of patients (35%) did have significant Late lesion growth despite successful recanalization (87%: 46% mTICI 2b/ 41% mTICI 3). Late lesion growth patients had a 27.4mL change in Late lesion volume and 30.1mL change in Early lesion volume. These patients had an increased hemorrhagic transformation rate of 68% with only 1 in 3 patients having favorable clinical outcome. Late lesion growth was independently associated with incomplete reperfusion, hemorrhagic transformation, and unfavorable outcome. Conclusion Approximately 1 out of 3 patients had Late lesion growth following EVT, with a favorable clinical outcome occurring in only 1 out of 3 of these patients. Most patients with no Early lesion growth had no Late lesion growth. Identification of patients with Late lesion growth could be critical to guide clinical management and inform prognosis post-EVT. Additionally, it can serve as an imaging biomarker for the development of adjunctive therapies to mitigate reperfusion injury.

Association of Multiple Passes during Mechanical Thrombectomy with Incomplete Reperfusion and Lesion Growth.

INTRODUCTION: Despite complete recanalization by mechanical thrombectomy, abnormal perfusion can be detected on MRI obtained post-endovascular therapy (EVT). The presence of residual perfusion abnormalities post-EVT may be associated with blood-brain barrier breakdown in response to mechanical disruption of the endothelium from multiple-pass thrombectomy. We hypothesize that multiple-pass versus single-pass thrombectomy is associated with a higher rate of residual hypoperfusion and increased lesion growth at 24 h. MATERIALS AND METHODS: For this analysis, we included patients presenting to one of two stroke centers between January 2015 and February 2018 with an acute ischemic stroke within 12 h from symptom onset if they had a large vessel occlusion of the anterior circulation documented on magnetic resonance angiography or CTA, baseline MRI pre-EVT with imaging evidence of hypoperfusion, underwent EVT, and had a post-EVT MRI with qualitatively interpretable perfusion-weighted imaging data at 24 h. MRI Tmax maps using a time delay threshold of >6 s were used to quantitate hypoperfusion volumes. Residual hypoperfusion at 24 h was solely defined as Tmax volume >10 mL with >6 s delay. Complete recanalization was defined as modified treatment in cerebral infarction visualized on angiography at EVT completion. Hyperintense acute reperfusion injury marker was assessed on post-EVT pre-contrast fluid-attenuated inversion recovery at 24 h. Major early neurological improvement was defined as a reduction of the admission National Institutes of Health Stroke Scale by ≥8 points or a score of 0-1 at 24 h. Good functional outcome was defined as 0-2 on the modified Rankin Scale on day 30 or 90. RESULTS: Fifty-five patients were included with median age 67 years, 58% female, 45% Black/African American, 36% White/Caucasian, median admission National Institutes of Health Stroke Scale 19, large vessel occlusion locations: 71% M1, 14.5% iICA, 14.5% M2, 69% treated with intravenous recombinant tissue plasminogen activator. Of these, 58% had multiple-pass thrombectomy, 39% had residual perfusion abnormalities at 24 h, and 64% had severe hyperintense acute reperfusion injury marker at 24 h. After adjusting for complete recanalization, only multiple-pass thrombectomy (odds ratio, 4.3 95% CI, 1.07-17.2; p = 0.04) was an independent predictor of residual hypoperfusion at 24 h. Patients with residual hypoperfusion had larger lesion growth on diffusion-weighted imaging (59 mL vs. 8 mL, p < 0.001), lower rate of major early neurological improvement (24% vs. 70%, p = 0.002) at 24 h, and worse long-term outcome based on the modified Rankin Scale at 30 or 90 days, 5 versus 2 (p < 0.001). CONCLUSIONS: Our findings suggest that incomplete reperfusion on post-EVT MRI is present even in some patients with successful recanalization at the time of EVT and is associated with multiple-pass thrombectomy, lesion growth, and worse outcome. Future studies are needed to investigate whether patients with residual hypoperfusion may benefit from immediate adjunctive therapy to limit lesion growth and improve clinical outcome.

Automatic quadriceps and patellae segmentation of MRI with cascaded U2 -Net and SASSNet deep learning model.

PURPOSE: Automatic muscle segmentation is critical for advancing our understanding of human physiology, biomechanics, and musculoskeletal pathologies, as it allows for timely exploration of large multi-dimensional image sets. Segmentation models are rarely developed/validated for the pediatric model. As such, autosegmentation is not available to explore how muscle architectural changes during development and how disease/pathology affects the developing musculoskeletal system. Thus, we aimed to develop and validate an end-to-end, fully automated, deep learning model for accurate segmentation of the rectus femoris and vastus lateral, medialis, and intermedialis using a pediatric database. METHODS: We developed a two-stage cascaded deep learning model in a coarse-to-fine manner. In the first stage, the U2 -Net roughly detects the muscle subcompartment region. Then, in the second stage, the shape-aware 3D semantic segmentation method SASSNet refines the cropped target regions to generate the more finer and accurate segmentation masks. We utilized multifeature image maps in both stages to stabilize performance and validated their use with an ablation study. The second-stage SASSNet was independently run and evaluated with three different cropped region resolutions: the original image resolution, and images downsampled 2× and 4× (high, mid, and low). The relationship between image resolution and segmentation accuracy was explored. In addition, the patella was included as a comparator to past work. We evaluated segmentation accuracy using leave-one-out testing on a database of 3D MR images (0.43 × 0.43 × 2 mm) from 40 pediatric participants (age 15.3 ± 1.9 years, 55.8 ± 11.8 kg, 164.2 ± 7.9 cm, 38F/2 M). RESULTS: The mid-resolution second stage produced the best results for the vastus medialis, rectus femoris, and patella (Dice similarity coefficient = 95.0%, 95.1%, 93.7%), whereas the low-resolution second stage produced the best results for the vastus lateralis and vastus intermedialis (DSC = 94.5% and 93.7%). In comparing the low- to mid-resolution cases, the vasti intermedialis, vastus medialis, rectus femoris, and patella produced significant differences (p = 0.0015, p = 0.0101, p < 0.0001, p = 0.0003) and the vasti lateralis did not (p = 0.2177). The high-resolution stage 2 had significantly lower accuracy (1.0 to 4.4 dice percentage points) compared to both the mid- and low-resolution routines (p value ranged from < 0.001 to 0.04). The one exception was the rectus femoris, where there was no difference between the low- and high-resolution cases. The ablation study demonstrated that the multifeature is more reliable than the single feature. CONCLUSIONS: Our successful implementation of this two-stage segmentation pipeline provides a critical tool for expanding pediatric muscle physiology and clinical research. With a relatively small and variable dataset, our fully automatic segmentation technique produces accuracies that matched or exceeded the current state of the art. The two-stage segmentation avoids memory issues and excessive run times by using a first stage focused on cropping out unnecessary data. The excellent Dice similarity coefficients improve upon previous template-based automatic and semiautomatic methodologies targeting the leg musculature. More importantly, with a naturally variable dataset (size, shape, etc.), the proposed model demonstrates slightly improved accuracies, compared to previous neural networks methods.

Dynamic morphogenesis of a pioneer axon in Drosophila and its regulation by Abl tyrosine kinase.

The fundamental problem in axon growth and guidance is to understand how cytoplasmic signaling modulates the cytoskeleton to produce directed growth cone motility. We here dissect this process using live imaging of the TSM1 axon of the developing Drosophila wing. We find that the growth cone is almost purely filopodial, and that it extends by a protrusive mode of growth. Quantitative analysis reveals two separate groups of growth cone properties that together account for growth cone structure and dynamics. The core morphological features of the growth cone are strongly correlated with one another and define two discrete morphs. Genetic manipulation of a critical mediator of axon guidance signaling, Abelson (Abl) tyrosine kinase, shows that while Abl weakly modulates the ratio of the two morphs it does not greatly change their properties. Rather, Abl primarily regulates the second group of properties, which report the organization and distribution of actin in the growth cone and are coupled to growth cone velocity. Other experiments dissect the nature of that regulation of actin organization and how it controls the spatial localization of filopodial dynamics and thus axon extension. Together, these observations suggest a novel, probabilistic mechanism by which Abl biases the stochastic fluctuations of growth cone actin to direct axon growth and guidance.

Abl signaling directs growth of a pioneer axon in Drosophila by shaping the intrinsic fluctuations of actin.

The fundamental problem in axon growth and guidance is understanding how cytoplasmic signaling modulates the cytoskeleton to produce directed growth cone motility. Live imaging of the TSM1 axon of the developing Drosophila wing has shown that the essential role of the core guidance signaling molecule, Abelson (Abl) tyrosine kinase, is to modulate the organization and spatial localization of actin in the advancing growth cone. Here, we dissect in detail the properties of that actin organization and its consequences for growth cone morphogenesis and motility. We show that advance of the actin mass in the distal axon drives the forward motion of the dynamic filopodial domain that defines the growth cone. We further show that Abl regulates both the width of the actin mass and its internal organization, spatially biasing the intrinsic fluctuations of actin to achieve net advance of the actin, and thus of the dynamic filopodial domain of the growth cone, while maintaining the essential coherence of the actin mass itself. These data suggest a model whereby guidance signaling systematically shapes the intrinsic, stochastic fluctuations of actin in the growth cone to produce axon growth and guidance.

Fully automated patellofemoral MRI segmentation using holistically nested networks: Implications for evaluating patellofemoral osteoarthritis, pain, injury, pathology, and adolescent development.

PURPOSE: Our clinical understanding of the relationship between 3D bone morphology and knee osteoarthritis, as well as our ability to investigate potential causative factors of osteoarthritis, has been hampered by the time-intensive nature of manually segmenting bone from MR images. Thus, we aim to develop and validate a fully automated deep learning framework for segmenting the patella and distal femur cortex, in both adults and actively growing adolescents. METHODS: Data from 93 subjects, obtained from on institutional review board-approved protocol, formed the study database. 3D sagittal gradient recalled echo and gradient recalled echo with fat saturation images and manual models of the outer cortex were available for 86 femurs and 90 patellae. A deep-learning-based 2D holistically nested network (HNN) architecture was developed to automatically segment the patella and distal femur using both single (sagittal, uniplanar) and 3 cardinal plane (triplanar) methodologies. Errors in the surface-to-surface distances and the Dice coefficient were the primary measures used to quantitatively evaluate segmentation accuracy using a 9-fold cross-validation. RESULTS: Average absolute errors for segmenting both the patella and femur were 0.33 mm. The Dice coefficients were 97% and 94% for the femur and patella. The uniplanar, relative to the triplanar, methodology produced slightly superior segmentation. Neither the presence of active growth plates nor pathology influenced segmentation accuracy. CONCLUSION: The proposed HNN with multi-feature architecture provides a fully automatic technique capable of delineating the often indistinct interfaces between the bone and other joint structures with an accuracy better than nearly all other techniques presented previously, even when active growth plates are present.

Fully automated prostate whole gland and central gland segmentation on MRI using holistically nested networks with short connections.

Accurate and automated prostate whole gland and central gland segmentations on MR images are essential for aiding any prostate cancer diagnosis system. Our work presents a 2-D orthogonal deep learning method to automatically segment the whole prostate and central gland from T2-weighted axial-only MR images. The proposed method can generate high-density 3-D surfaces from low-resolution ( z axis) MR images. In the past, most methods have focused on axial images alone, e.g., 2-D based segmentation of the prostate from each 2-D slice. Those methods suffer the problems of over-segmenting or under-segmenting the prostate at apex and base, which adds a major contribution for errors. The proposed method leverages the orthogonal context to effectively reduce the apex and base segmentation ambiguities. It also overcomes jittering or stair-step surface artifacts when constructing a 3-D surface from 2-D segmentation or direct 3-D segmentation approaches, such as 3-D U-Net. The experimental results demonstrate that the proposed method achieves 92.4 % ± 3 % Dice similarity coefficient (DSC) for prostate and DSC of 90.1 % ± 4.6 % for central gland without trimming any ending contours at apex and base. The experiments illustrate the feasibility and robustness of the 2-D-based holistically nested networks with short connections method for MR prostate and central gland segmentation. The proposed method achieves segmentation results on par with the current literature.

Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks.

Accurate automatic segmentation of the prostate in magnetic resonance images (MRI) is a challenging task due to the high variability of prostate anatomic structure. Artifacts such as noise and similar signal intensity of tissues around the prostate boundary inhibit traditional segmentation methods from achieving high accuracy. We investigate both patch-based and holistic (image-to-image) deep-learning methods for segmentation of the prostate. First, we introduce a patch-based convolutional network that aims to refine the prostate contour which provides an initialization. Second, we propose a method for end-to-end prostate segmentation by integrating holistically nested edge detection with fully convolutional networks. Holistically nested networks (HNN) automatically learn a hierarchical representation that can improve prostate boundary detection. Quantitative evaluation is performed on the MRI scans of 250 patients in fivefold cross-validation. The proposed enhanced HNN model achieves a mean ± standard deviation. A Dice similarity coefficient (DSC) of [Formula: see text] and a mean Jaccard similarity coefficient (IoU) of [Formula: see text] are used to calculate without trimming any end slices. The proposed holistic model significantly ([Formula: see text]) outperforms a patch-based AlexNet model by 9% in DSC and 13% in IoU. Overall, the method achieves state-of-the-art performance as compared with other MRI prostate segmentation methods in the literature.

Analyzing Dendritic Morphology in Columns and Layers.

In many regions of the central nervous systems, such as the fly optic lobes and the vertebrate cortex, synaptic circuits are organized in layers and columns to facilitate brain wiring during development and information processing in developed animals. Postsynaptic neurons elaborate dendrites in type-specific patterns in specific layers to synapse with appropriate presynaptic terminals. The fly medulla neuropil is composed of 10 layers and about 750 columns; each column is innervated by dendrites of over 38 types of medulla neurons, which match with the axonal terminals of some 7 types of afferents in a type-specific fashion. This report details the procedures to image and analyze dendrites of medulla neurons. The workflow includes three sections: (i) the dual-view imaging section combines two confocal image stacks collected at orthogonal orientations into a high-resolution 3D image of dendrites; (ii) the dendrite tracing and registration section traces dendritic arbors in 3D and registers dendritic traces to the reference column array; (iii) the dendritic analysis section analyzes dendritic patterns with respect to columns and layers, including layer-specific termination and planar projection direction of dendritic arbors, and derives estimates of dendritic branching and termination frequencies. The protocols utilize custom plugins built on the open-source MIPAV (Medical Imaging Processing, Analysis, and Visualization) platform and custom toolboxes in the matrix laboratory language. Together, these protocols provide a complete workflow to analyze the dendritic routing of Drosophila medulla neurons in layers and columns, to identify cell types, and to determine defects in mutants.

Untwisting the Caenorhabditis elegans embryo.

The nematode Caenorhabditis elegans possesses a simple embryonic nervous system with few enough neurons that the growth of each cell could be followed to provide a systems-level view of development. However, studies of single cell development have largely been conducted in fixed or pre-twitching live embryos, because of technical difficulties associated with embryo movement in late embryogenesis. We present open-source untwisting and annotation software (http://mipav.cit.nih.gov/plugin_jws/mipav_worm_plugin.php) that allows the investigation of neurodevelopmental events in late embryogenesis and apply it to track the 3D positions of seam cell nuclei, neurons, and neurites in multiple elongating embryos. We also provide a tutorial describing how to use the software (Supplementary file 1) and a detailed description of the untwisting algorithm (Appendix). The detailed positional information we obtained enabled us to develop a composite model showing movement of these cells and neurites in an 'average' worm embryo. The untwisting and cell tracking capabilities of our method provide a foundation on which to catalog C. elegans neurodevelopment, allowing interrogation of developmental events in previously inaccessible periods of embryogenesis.

Dual-view plane illumination microscopy for rapid and spatially isotropic imaging.

We describe the construction and use of a compact dual-view inverted selective plane illumination microscope (diSPIM) for time-lapse volumetric (4D) imaging of living samples at subcellular resolution. Our protocol enables a biologist with some prior microscopy experience to assemble a diSPIM from commercially available parts, to align optics and test system performance, to prepare samples, and to control hardware and data processing with our software. Unlike existing light sheet microscopy protocols, our method does not require the sample to be embedded in agarose; instead, samples are prepared conventionally on glass coverslips. Tissue culture cells and Caenorhabditis elegans embryos are used as examples in this protocol; successful implementation of the protocol results in isotropic resolution and acquisition speeds up to several volumes per s on these samples. Assembling and verifying diSPIM performance takes ∼6 d, sample preparation and data acquisition take up to 5 d and postprocessing takes 3-8 h, depending on the size of the data.

Atlas based AAM and SVM model for fully automatic MRI prostate segmentation.

Automatic prostate segmentation in MR images is a challenging task due to inter-patient prostate shape and texture variability, and the lack of a clear prostate boundary. We propose a supervised learning framework that combines the atlas based AAM and SVM model to achieve a relatively high segmentation result of the prostate boundary. The performance of the segmentation is evaluated with cross validation on 40 MR image datasets, yielding an average segmentation accuracy near 90%.

GPU based multi-histogram volume navigation for virtual bronchoscopy.

An interactive navigation system for virtual bronchoscopy is presented, which is based solely on GPU based high performance multi-histogram volume rendering.

Interfaces and Integration of Medical Image Analysis Frameworks: Challenges and Opportunities.

Clinical research with medical imaging typically involves large-scale data analysis with interdependent software toolsets tied together in a processing workflow. Numerous, complementary platforms are available, but these are not readily compatible in terms of workflows or data formats. Both image scientists and clinical investigators could benefit from using the framework which is a most natural fit to the specific problem at hand, but pragmatic choices often dictate that a compromise platform is used for collaboration. Manual merging of platforms through carefully tuned scripts has been effective, but exceptionally time consuming and is not feasible for large-scale integration efforts. Hence, the benefits of innovation are constrained by platform dependence. Removing this constraint via integration of algorithms from one framework into another is the focus of this work. We propose and demonstrate a light-weight interface system to expose parameters across platforms and provide seamless integration. In this initial effort, we focus on four platforms Medical Image Analysis and Visualization (MIPAV), Java Image Science Toolkit (JIST), command line tools, and 3D Slicer. We explore three case studies: (1) providing a system for MIPAV to expose internal algorithms and utilize these algorithms within JIST, (2) exposing JIST modules through self-documenting command line interface for inclusion in scripting environments, and (3) detecting and using JIST modules in 3D Slicer. We review the challenges and opportunities for light-weight software integration both within development language (e.g., Java in MIPAV and JIST) and across languages (e.g., C/C++ in 3D Slicer and shell in command line tools).

Using global unique identifiers to link autism collections.

OBJECTIVE: To propose a centralized method for generating global unique identifiers to link collections of research data and specimens. DESIGN: The work is a collaboration between the Simons Foundation Autism Research Initiative and the National Database for Autism Research. The system is implemented as a web service: an investigator inputs identifying information about a participant into a client application and sends encrypted information to a server application, which returns a generated global unique identifier. The authors evaluated the system using a volume test of one million simulated individuals and a field test on 2000 families (over 8000 individual participants) in an autism study. MEASUREMENTS: Inverse probability of hash codes; rate of false identity of two individuals; rate of false split of single individual; percentage of subjects for which identifying information could be collected; percentage of hash codes generated successfully. RESULTS: Large-volume simulation generated no false splits or false identity. Field testing in the Simons Foundation Autism Research Initiative Simplex Collection produced identifiers for 96% of children in the study and 77% of parents. On average, four out of five hash codes per subject were generated perfectly (only one perfect hash is required for subsequent matching). DISCUSSION: The system must achieve balance among the competing goals of distinguishing individuals, collecting accurate information for matching, and protecting confidentiality. Considerable effort is required to obtain approval from institutional review boards, obtain consent from participants, and to achieve compliance from sites during a multicenter study. CONCLUSION: Generic unique identifiers have the potential to link collections of research data, augment the amount and types of data available for individuals, support detection of overlap between collections, and facilitate replication of research findings.

Technologies for guidance of radiofrequency ablation in the multimodality interventional suite of the future.

Several new image-guidance tools and devices are being prototyped, investigated, and compared. These tools are introduced and include prototype software for image registration and fusion, thermal modeling, electromagnetic tracking, semiautomated robotic needle guidance, and multimodality imaging. The integration of treatment planning with computed tomography robot systems or electromagnetic needle-tip tracking allows for seamless, iterative, "see-and-treat," patient-specific tumor ablation. Such automation, navigation, and visualization tools could eventually optimize radiofrequency ablation and other needle-based ablation procedures and decrease variability among operators, thus facilitating the translation of novel image-guided therapies. Much of this new technology is in use or will be available to the interventional radiologist in the near future, and this brief introduction will hopefully encourage research in this emerging area.

Radio frequency ablation registration, segmentation, and fusion tool.

The radio frequency ablation segmentation tool (RFAST) is a software application developed using the National Institutes of Health's medical image processing analysis and visualization (MIPAV) API for the specific purpose of assisting physicians in the planning of radio frequency ablation (RFA) procedures. The RFAST application sequentially leads the physician through the steps necessary to register, fuse, segment, visualize, and plan the RFA treatment. Three-dimensional volume visualization of the CT dataset with segmented three dimensional (3-D) surface models enables the physician to interactively position the ablation probe to simulate burns and to semimanually simulate sphere packing in an attempt to optimize probe placement. This paper describes software systems contained in RFAST to address the needs of clinicians in planning, evaluating, and simulating RFA treatments of malignant hepatic tissue.