Modeling the Intra-class Variability for Liver Lesion Detection using a Multi-class Patch-based CNN

Automatic detection of liver lesions in CT images poses a great challenge for researchers. In this work we present a deep learning approach that models explicitly the variability within the non-lesion class, based on prior knowledge of the data, to support an automated lesion detection system. A multi-class convolutional neural network (CNN) is proposed to categorize input image patches into sub-categories of boundary and interior patches, the decisions of which are fused to reach a binary lesion vs non-lesion decision. For validation of our system, we use CT images of 132 livers and 498 lesions. Our approach shows highly improved detection results that outperform the state-of-the-art fully convolutional network. Automated computerized tools, as shown in this work, have the potential in the future to support the radiologists towards improved detection.Comment: To be presented at PatchMI: 3rd International Workshop on Patch-based Techniques in Medical Imaging, MICCAI 201

Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification

In this paper, we present a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs). We propose a training scheme that first uses classical data augmentation to enlarge the training set and then further enlarges the data size and its diversity by applying GAN techniques for synthetic data augmentation. Our method is demonstrated on a limited dataset of computed tomography (CT) images of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). The classification performance using only classic data augmentation yielded 78.6% sensitivity and 88.4% specificity. By adding the synthetic data augmentation the results significantly increased to 85.7% sensitivity and 92.4% specificity.Comment: To be presented at IEEE International Symposium on Biomedical Imaging (ISBI), 201

Anatomical Data Augmentation For CNN based Pixel-wise Classification

In this work we propose a method for anatomical data augmentation that is based on using slices of computed tomography (CT) examinations that are adjacent to labeled slices as another resource of labeled data for training the network. The extended labeled data is used to train a U-net network for a pixel-wise classification into different hepatic lesions and normal liver tissues. Our dataset contains CT examinations from 140 patients with 333 CT images annotated by an expert radiologist. We tested our approach and compared it to the conventional training process. Results indicate superiority of our method. Using the anatomical data augmentation we achieved an improvement of 3% in the success rate, 5% in the classification accuracy, and 4% in Dice.Comment: To be presented at IEEE ISBI 201

Local dynamics of gap-junction-coupled interneuron networks

Interneurons coupled by both electrical gap-junctions (GJs) and chemical GABAergic synapses are major components of forebrain networks. However, their contributions to the generation of specific activity patterns, and their overall contributions to network function, remain poorly understood. Here we demonstrate, using computational methods, that the topological properties of interneuron networks can elicit a wide range of activity dynamics, and either prevent or permit local pattern formation. We systematically varied the topology of GJ and inhibitory chemical synapses within simulated networks, by changing connection types from local to random, and changing the total number of connections. As previously observed we found that randomly coupled GJs lead to globally synchronous activity. In contrast, we found that local GJ connectivity may govern the formation of highly spatially heterogeneous activity states. These states are inherently temporally unstable when the input is uniformly random, but can rapidly stabilize when the network detects correlations or asymmetries in the inputs. We show a correspondence between this feature of network activity and experimental observations of transient stabilization of striatal fast-spiking interneurons (FSIs), in electrophysiological recordings from rats performing a simple decision-making task. We suggest that local GJ coupling enables an active search-and-select function of striatal FSIs, which contributes to the overall role of cortical-basal ganglia circuits in decision-making.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85426/1/ph10_1_016015.pd

Perianal Pediatric Crohn Disease Is Associated With a Distinct Phenotype and Greater Inflammatory Burden

Objectives: Data on the outcomes of children with perianal Crohn disease (pCD) are limited, although its presence is often used for justifying early use of biologics. We aimed to assess whether pCD in children is associated with more severe outcomes as found in adults. Methods: Data were extracted from the ImageKids database, a prospective, multicenter, longitudinal cohort study. The study enrolled 246 children at disease onset or thereafter. All patients underwent comprehensive clinical, endoscopic, and radiologic evaluation at enrollment;98 children had repeat evaluation at 18 months. Results: Of the 234 included patients (mean age 14.2 +/- 2.4 years;131 [56%] boys), 57 (24%) had perianal findings, whereas only 21 (9%) had fistulizing perianal disease. Children with pCD had reduced weight and height z scores compared with non-pCD patients (-0.9 vs -0.35, P = 0.03 and -0.68 vs -0.23, respectively;P = 0.04), higher weighted pediatric CD activity index (32 [interquartile range 16-50] vs 20 [8-37];P = 0.004), lower serum albumin (3.6 +/- 0.7 vs 4.5 +/- 0.8, P = 0.016), and higher magnetic resonance enterography global inflammatory score (P = 0.04). Children with pCD had more rectal (57% vs 38%, P = 0.04), and jejunal involvement (31% vs 11% P = 0.003) and a higher prevalence of granulomas (64% vs 23%, P = 0.0001). Magnetic resonance enterography-based damage scores did not differ between groups. Patients with skin tags/fissures only, had similar clinical, endoscopic, and radiologic characteristics as patients with no perianal findings. Conclusions: Pediatric patients with pCD with fistulizing disease have distinct phenotypic features and a predisposition to a greater inflammatory burden

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094