Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions

In recent years, deep learning-based networks have achieved state-of-the-art performance in medical image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net, Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which we take full advantages of U-Net, bi-directional ConvLSTM (BConvLSTM) and the mechanism of dense convolutions. Instead of a simple concatenation in the skip connection of U-Net, we employ BConvLSTM to combine the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional layer in a non-linear way. To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path. Finally, we can accelerate the convergence speed of the proposed network by employing batch normalization (BN). The proposed model is evaluated on three datasets of: retinal blood vessel segmentation, skin lesion segmentation, and lung nodule segmentation, achieving state-of-the-art performance

Two Stream Auto-encoder Decoder Network for Kidney and Tumor Segmentation

In this competition, we apply two-streams auto-encoder decoder structure for learning kidney and kidneys tumor segmentation. To do so, first, we extract axial layers of the tissues along with their segmentation mask from the 3D volume. These axial layers are then clipped using Hanford distance between +512 to -512 to eliminate non-object of interest. These axial layers are then normalized to form the 2D grayscale images. For each of these normalized images, we generate kidney and kidney tumor masks to train two-stream deep networks. The two-streams deep model learns kidney and tumor masks separately and they generate final mask by concatenating the generated masks. We utilize BCDU-net (extended version of U-Net model) as a deep auto-encoder decoder model for segmentation. We utilize 70% of the Kits19 as the training set and the rest of data as the validation set. Experimental results demonstrate that the proposed structure achieves state-of-the-art performance in the segmentation of kidney and tumor region

Stacked BCDU-Net with Semantic CMR Synthesis: Application to Myocardial Pathology Segmentation Challenge

Accurate segmentation of pathological tissue, such as scar tissue and edema, from cardiac magnetic resonance images (CMR) is fundamental to the assessment of the severity of myocardial infarction and myocardial viability. There are many accurate solutions for auto- matic segmentation of cardiac structures from CMR. On the contrary, a solution has not as yet been found for the automatic segmentation of my- ocardial pathological regions due to their challenging nature. As part of the Myocardial Pathology Segmentation combining multi-sequence CMR (MyoPS) challenge, we propose a fully automatic pipeline for segment- ing pathological tissue using registered multi-sequence CMR images se- quences (LGE, bSSFP and T2). The proposed approach involves a two- staged process. First, in order to reduce task complexity, a two-stacked BCDU-net is proposed to a) detect a small ROI based on accurate my- ocardium segmentation and b) perform inside-ROI multi-modal patho- logical region segmentation. Second, in order to regularize the proposed stacked architecture and deal with the under-represented data prob- lem, we propose a synthetic data augmentation pipeline that generates anatomically meaningful samples. The outputs of the proposed stacked BCDU-NET with semantic CMR synthesis are post-processed based on anatomical constrains to re ne output segmentation masks. Results from 25 di erent patients demonstrate that the proposed model improves 1- stage equivalent architectures and bene ts from the addition of synthetic anatomically meaningful samples. A  nal ensemble of 15 trained models show a challenge Dice test score of 0.665 0.143 and 0.698 0.128 for scar and scar+edema, respectively

A Survey on Deep Learning Based Approaches for Action and Gesture Recognition in Image Sequences

International audienceThe interest in action and gesture recognition has grown considerably in the last years. In this paper, we present a survey on current deep learning methodologies for action and gesture recognition in image sequences. We introduce a taxonomy that summarizes important aspects of deep learning for approaching both tasks. We review the details of the proposed architectures, fusion strategies, main datasets, and competitions. We summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, discussing their main features and identify opportunities and challenges for future research

Automatic access control based on face and hand biometrics in a non-cooperative context

Automatic access control systems (ACS) based on the human biometrics or physical tokens are widely employed in public and private areas. Yet these systems, in their conventional forms, are restricted to active interaction from the users. In scenarios where users are not cooperating with the system, these systems are challenged. Failure in cooperation with the biometric systems might be intentional or because the users are incapable of handling the interaction procedure with the biometric system or simply forget to cooperate with it, due to for example, illness like dementia. This work introduces a challenging bimodal database, including face and hand information of the users when they approach a door to open it by its handle in a noncooperative context. We have defined two (an easy and a challenging) protocols on how to use the database. We have reported results on many baseline methods, including deep learning techniques as well as conventional methods on the database. The obtained results show the merit of the proposed database and the challenging nature of access control with non-cooperative users