CTransNet: Convolutional Neural Network Combined with Transformer for Medical Image Segmentation

The Transformer has been widely used for many tasks in NLP before, but there is still much room to explore the application of the Transformer to the image domain. In this paper, we propose a simple and efficient hybrid Transformer framework, CTransNet, which combines self-attention and CNN to improve medical image segmentation performance. Capturing long-range dependencies at different scales. To this end, this paper proposes an effective self-attention mechanism incorporating relative position information encoding, which can reduce the time complexity of self-attention from O(n2) to O(n), and a new self-attention decoder that can recover fine-grained features in encoder from skip connection. This paper aims to address the current dilemma of Transformer applications: i.e., the need to learn induction bias from large amounts of training data. The hybrid layer in CTransNet allows the Transformer to be initialized as a CNN without pre-training. We have evaluated the performance of CTransNet on several medical segmentation datasets. CTransNet shows superior segmentation performance, robustness, and great promise for generalization to other medical image segmentation tasks

A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks

Deep LSTM is an ideal candidate for text recognition. However text recognition involves some initial image processing steps like segmentation of lines and words which can induce error to the recognition system. Without segmentation, learning very long range context is difficult and becomes computationally intractable. Therefore, alternative soft decisions are needed at the pre-processing level. This paper proposes a hybrid text recognizer using a deep recurrent neural network with multiple layers of abstraction and long range context along with a language model to verify the performance of the deep neural network. In this paper we construct a multi-hypotheses tree architecture with candidate segments of line sequences from different segmentation algorithms at its different branches. The deep neural network is trained on perfectly segmented data and tests each of the candidate segments, generating unicode sequences. In the verification step, these unicode sequences are validated using a sub-string match with the language model and best first search is used to find the best possible combination of alternative hypothesis from the tree structure. Thus the verification framework using language models eliminates wrong segmentation outputs and filters recognition errors

Statistical Model of Shape Moments with Active Contour Evolution for Shape Detection and Segmentation

This paper describes a novel method for shape representation and robust image segmentation. The proposed method combines two well known methodologies, namely, statistical shape models and active contours implemented in level set framework. The shape detection is achieved by maximizing a posterior function that consists of a prior shape probability model and image likelihood function conditioned on shapes. The statistical shape model is built as a result of a learning process based on nonparametric probability estimation in a PCA reduced feature space formed by the Legendre moments of training silhouette images. A greedy strategy is applied to optimize the proposed cost function by iteratively evolving an implicit active contour in the image space and subsequent constrained optimization of the evolved shape in the reduced shape feature space. Experimental results presented in the paper demonstrate that the proposed method, contrary to many other active contour segmentation methods, is highly resilient to severe random and structural noise that could be present in the data