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With the aid of digital inpainting algorithms, this paper presents a novel application that uses image segmentation to remove surgical instruments from laparoscopic/endoscopic video.

[paper][code][dataset][bibtex][Video 1]

In this paper, we propose a semi-supervised model --- namely, Combine-all in Semi-Supervised Learning (CqSL) --- to demonstrate the power of a simple combination of a disentanglement block, variational autoencoder (VAE), generative adversarial network (GAN), and a conditioning layer-based reconstructor for performing two important tasks in medical imaging: segmentation and reconstruction. Our work is motivated by the recent progress in image segmentation using semi-supervised learning (SSL), which has shown good results with limited labeled data and large amounts of unlabeled data.

[paper][code][dataset][bibtex][Video 1]

In this paper, we study this hypothesis using two publicly available datasets: a 2D organ classification dataset with target organ classes being visually distinct, and a histopathology image classification dataset where the target classes look very similar visually. Our results show that the label noise in one class has much higher impact on the model's performance on other classes for histopathology dataset compared to the organ dataset.

[paper][code][dataset][bibtex][Video 1]

The proposed method uses self-training (through meta pseudo-labeling) in concert with a Teacher network that instructs the Student network by generating pseudo-labels given unlabeled input data. Meta pseudo-labeling methods allow the Teacher network to constantly adapt in response to the performance of the Student network on the labeled dataset, hence enabling the Teacher to identify more effective pseudo-labels to instruct the Student. Moreover, to improve generalization and reduce error rate, we apply both strong and weak data augmentation policies, to ensure the segmentor outputs a consistent probability distribution regardless of the augmentation level.

[paper][code][dataset][bibtex][Video 1]

In this work, we used a Bayesian version of our previously proposed CondenseUNet framework featuring both a learned group structure and a regularized weight-pruner to reduce the computational cost in volumetric image segmentation and help quantify predictive uncertainty. Our study further showcases the potential of our deep-learning framework to evaluate the correlation between the uncertainty and the segmentation errors for a given model. The proposed model was trained and tested on the Automated Cardiac Diagnosis Challenge (ACDC) dataset featuring 150 cine cardiac MRI patient dataset for the segmentation and uncertainty estimation of the left ventricle (LV), right ventricle (RV), and myocardium (Myo) at end-diastole (ED) and end-systole (ES) phases.

[paper][code][dataset][bibtex][Video 1]

In this work we propose a novel method that incorporates uncertainty estimation to detect failures in the segmentation masks generated by CNNs, our study further showcases the potential of our model to evaluate the correlation between the uncertainty and the segmentation errors for a given model. Furthermore, we introduce a multi-task Cross-task learning consistency approach to enforce the correlation between the pixel-level (segmentation) and the geometric-level (distance map) tasks. Our study serves as a proof-of-concept of how uncertainty measure correlates with the erroneous segmentation generated by different deep learning models, further showcasing the potential of our model to flag low-quality segmentation from a given model in our future study.

[paper][code][dataset][bibtex][Video 1]

In this work, we describe the development of dynamic patient-specific right ventricle (RV) models associated with normal subjects and abnormal RV patients to be subsequently used to assess RV function based on motion and kinematic analysis. In our study, we use a deep learning-based deformable network that takes 3D input volumes and outputs a motion field which is then used to generate isosurface meshes of the cardiac geometry at all cardiac frames by propagating the end-diastole (ED) isosurface mesh using the reconstructed motion field.

[paper][code][dataset][bibtex][Video 1]

To generate smoother and accurate segmentation masks from 3D cardiac MR images, we present a Multi-task Cross-task learning consistency approach to enforce the correlation between the pixel-level (segmentation) and the geometric-level (distance map) tasks. Our extensive experimentation with varied quantities of labeled data in the training sets justify the effectiveness of our model for the segmentation of left atrial cavity from Gadolinium-enhanced magnetic resonance (GE-MR) images. With the incorporation of uncertainty estimates to detect failures in the segmentation masks generated by CNNs, our study further showcases the potential of our model to flag low quality segmentation from a given model.

[paper][code][dataset][bibtex][Video 1] [Video 2 ][Video 3] [Video 4]

In this work, we implement a fully convolutional segmenter featuring both a learned group structure and a regularized weight-pruner to reduce the high computational cost in volumetric image segmentation. We validated our framework on the ACDC dataset featuring one healthy and four pathology groups imaged throughout the cardiac cycle. Based on these results, this technique has the potential to become an efficient and competitive cardiac image segmentation tool that may be used for cardiac computer-aided diagnosis, planning and guidance applications.

[paper][code][dataset][bibtex]

In this work, we implement a fully convolutional segmenter featuring both a learned group structure and a regularized weight-pruner to reduce the high computational cost in volumetric image segmentation. We validated our framework on the ACDC dataset featuring one healthy and four pathology groups imaged throughout the cardiac cycle. Our technique achieved Dice scores of 96.8% (LV blood-pool), 93.3% (RV blood-pool) and 90.0% (LV Myocardium) with five-fold cross-validation and yielded similar clinical parameters as those estimated from the ground truth segmentation data. Based on these results, this technique has the potential to become an efficient and competitive cardiac image segmentation tool that may be used for cardiac computer-aided diagnosis, planning, and guidance applications.

[paper][code][dataset][bibtex]

In this work, we implement a fully convolutional segmenter featuring both a learned group structure and a regularized weight-pruner to reduce the high computational cost in volumetric image segmentation. We validated the framework on the ACDC dataset and achieved accurate segmentation, leading to mean Dice scores of 96.80% (LV blood-pool), 93.33% (RV blood-pool), 90.0% (LV Myocardium) and yielded similar clinical parameters as those estimated from the ground-truth segmentation data.

[paper][code][dataset][bibtex]

In this work, we propose a novel memory-efficient Convolutional Neural Network (CNN) architecture as a modification of both CondenseNet, as well as DenseNet for ventricular blood-pool segmentation by introducing a bottleneck block and an upsampling path. Our experiments show that the proposed architecture runs on the Automated Cardiac Diagnosis Challenge (ACDC) dataset using half (50%) the memory requirement of DenseNet and one-twelfth (∼ 8%) of the memory requirements of U-Net, while still maintaining excellent accuracy of cardiac segmentation.

[paper][code][dataset][bibtex]

In the forward model, tissue deformation was generated using a test wave with active stresses that mimic the myocardial contractile forces. The generated deformation field was used as input to an inverse model designed to reconstruct the original active stress distribution. We numerically simulated malfunctioning tissue regions (experiencing limited contractility and hence active stress) within the healthy tissue. We also assessed model sensitivity by adding noise to the deformation field generated using the forward model. The difference image between the original and reconstructed active stress distribution suggests that the model accurately estimates active stress from tissue deformation data with a high signal-to-noise ratio.

[paper][code][dataset][bibtex]

In this work, we modify the U-Net architecture by introducing a pre-trained encoder and re-design the decoder part, by replacing the transposed convolution operation with an upsampling operation based on nearest-neighbor (NN) interpolation. To further improve performance, we also employ a very fast and flexible data augmentation technique. We trained the framework on 8 x 225 frame sequences of robotic surgical videos available through the MICCAI 2017 EndoVis Challenge dataset and tested it on 8 x 75 frame and 2 x 300 frame videos. Using our U-NetPlus architecture, we report a 90.20\% DICE for binary segmentation, 76.26% DICE for instrument part segmentation, and 46.07% for instrument type (i.e., all instruments) segmentation, outperforming the results of previous techniques implemented and tested on these data.

[paper][code][dataset][bibtex]

Though this model works better on BRATS 2015 dataset by using pixel-wise segmentation map of the input image like an auto-encoder which assures best segmentation accuracy, but it is not correct for all the cases. So, I have planned to improve this U-net model by replacing the de-convolution part with the upsampled by Nearest-neighbor algorithm and also by using elastic transformation for increasing the training dataset to make the model more robust on Low graded tumor. I had trained my NNRET U-net model on BRATS 2017 dataset and got a better performance than the state of the art classic U-net model.