PocketNet: A Smaller Neural Network for Medical Image Analysis

Medical imaging deep learning models are often large and complex, requiring specialized hardware to train and evaluate these models. To address such issues, we propose the PocketNet paradigm to reduce the size of deep learning models by throttling the growth of the number of channels in convolutional neural networks. We demonstrate that, for a range of segmentation and classification tasks, PocketNet architectures produce results comparable to that of conventional neural networks while reducing the number of parameters by multiple orders of magnitude, using up to 90% less GPU memory, and speeding up training times by up to 40%, thereby allowing such models to be trained and deployed in resource-constrained settings

Strengthening deep-learning models for intracranial hemorrhage detection: strongly annotated computed tomography images and model ensembles

Background and purposeMultiple attempts at intracranial hemorrhage (ICH) detection using deep-learning techniques have been plagued by clinical failures. We aimed to compare the performance of a deep-learning algorithm for ICH detection trained on strongly and weakly annotated datasets, and to assess whether a weighted ensemble model that integrates separate models trained using datasets with different ICH improves performance.MethodsWe used brain CT scans from the Radiological Society of North America (27,861 CT scans, 3,528 ICHs) and AI-Hub (53,045 CT scans, 7,013 ICHs) for training. DenseNet121, InceptionResNetV2, MobileNetV2, and VGG19 were trained on strongly and weakly annotated datasets and compared using independent external test datasets. We then developed a weighted ensemble model combining separate models trained on all ICH, subdural hemorrhage (SDH), subarachnoid hemorrhage (SAH), and small-lesion ICH cases. The final weighted ensemble model was compared to four well-known deep-learning models. After external testing, six neurologists reviewed 91 ICH cases difficult for AI and humans.ResultsInceptionResNetV2, MobileNetV2, and VGG19 models outperformed when trained on strongly annotated datasets. A weighted ensemble model combining models trained on SDH, SAH, and small-lesion ICH had a higher AUC, compared with a model trained on all ICH cases only. This model outperformed four deep-learning models (AUC [95% C.I.]: Ensemble model, 0.953[0.938–0.965]; InceptionResNetV2, 0.852[0.828–0.873]; DenseNet121, 0.875[0.852–0.895]; VGG19, 0.796[0.770–0.821]; MobileNetV2, 0.650[0.620–0.680]; p < 0.0001). In addition, the case review showed that a better understanding and management of difficult cases may facilitate clinical use of ICH detection algorithms.ConclusionWe propose a weighted ensemble model for ICH detection, trained on large-scale, strongly annotated CT scans, as no model can capture all aspects of complex tasks

The Emerging Role of Amino Acid PET in Neuro-Oncology

Imaging plays a critical role in the management of the highly complex and widely diverse central nervous system (CNS) malignancies in providing an accurate diagnosis, treatment planning, response assessment, prognosis, and surveillance. Contrast-enhanced magnetic resonance imaging (MRI) is the primary modality for CNS disease management due to its high contrast resolution, reasonable spatial resolution, and relatively low cost and risk. However, defining tumor response to radiation treatment and chemotherapy by contrast-enhanced MRI is often difficult due to various factors that can influence contrast agent distribution and perfusion, such as edema, necrosis, vascular alterations, and inflammation, leading to pseudoprogression and pseudoresponse assessments. Amino acid positron emission tomography (PET) is emerging as the method of resolving such equivocal lesion interpretations. Amino acid radiotracers can more specifically differentiate true tumor boundaries from equivocal lesions based on their specific and active uptake by the highly metabolic cellular component of CNS tumors. These therapy-induced metabolic changes detected by amino acid PET facilitate early treatment response assessments. Integrating amino acid PET in the management of CNS malignancies to complement MRI will significantly improve early therapy response assessment, treatment planning, and clinical trial design

Impairment of retrograde neuronal transport in oxaliplatin-induced neuropathy demonstrated by molecular imaging.

Background and purposeThe purpose of our study was to utilize a molecular imaging technology based on the retrograde axonal transport mechanism (neurography), to determine if oxaliplatin-induced neurotoxicity affects retrograde axonal transport in an animal model.Materials and methodsMice (n = 8/group) were injected with a cumulative dose of 30 mg/kg oxaliplatin (sufficient to induce neurotoxicity) or dextrose control injections. Intramuscular injections of Tetanus Toxin C-fragment (TTc) labeled with Alexa 790 fluorescent dye were done (15 ug/20 uL) in the left calf muscles, and in vivo fluorescent imaging performed (0-60 min) at baseline, and then weekly for 5 weeks, followed by 2-weekly imaging out to 9 weeks. Tissues were harvested for immunohistochemical analysis.ResultsWith sham treatment, TTc transport causes fluorescent signal intensity over the thoracic spine to increase from 0 to 60 minutes after injection. On average, fluorescence signal increased 722%+/-117% (Mean+/-SD) from 0 to 60 minutes. Oxaliplatin treated animals had comparable transport at baseline (787%+/-140%), but transport rapidly decreased through the course of the study, falling to 363%+/-88%, 269%+/-96%, 191%+/-58%, 121%+/-39%, 75%+/-21% with each successive week and stabilizing around 57% (+/-15%) at 7 weeks. Statistically significant divergence occurred at approximately 3 weeks (p≤0.05, linear mixed-effects regression model). Quantitative immuno-fluorescence histology with a constant cutoff threshold showed reduced TTc in the spinal cord at 7 weeks for treated animals versus controls (5.2 Arbitrary Units +/-0.52 vs 7.1 AU +/-1.38, p0.56, T-test).ConclusionWe show-for the first time to our knowledge-that neurographic in vivo molecular imaging can demonstrate imaging changes in a model of oxaliplatin-induced neuropathy. Impaired retrograde neural transport is suggested to be an important part of the pathophysiology of oxaliplatin-induced neuropathy

Fluorescence Imaging of Fast Retrograde Axonal Transport in Living Animals

Our purpose was to enable an in vivo imaging technology that can assess the anatomy and function of peripheral nerve tissue (neurography). To do this, we designed and tested a fluorescently labeled molecular probe based on the nontoxic C fragment of tetanus toxin (TTc). TTc was purified, labeled, and subjected to immunoassays and cell uptake assays. The compound was then injected into C57BL/6 mice (N = 60) for in vivo imaging and histologic studies. Image analysis and immunohistochemistry were performed. We found that TTc could be labeled with fluorescent moieties without loss of immunoreactivity or biologic potency in cell uptake assays. In vivo fluorescent imaging experiments demonstrated uptake and retrograde transport of the compound along the course of the sciatic nerve and in the spinal cord. Ex vivo imaging and immunohistochemical studies confirmed the presence of TTc in the sciatic nerve and spinal cord, whereas control animals injected with human serum albumin did not exhibit these features. We have demonstrated neurography with a fluorescently labeled molecular imaging contrast agent based on the TTc