Recent trend in medical imaging modalities and their applications in disease diagnosis: a review

Deep learning shows excellent performance in medical image segmentation. However, a pooling operation in its encoding stage leads to feature loss and the ability of multi-scale contextual information extraction is insufficient. In addition, there is a semantic gap between low-level features and high-level features in the encoder–decoder. Inspired by an U-shaped network (UNet), this work proposes a multi-scale dilation attention network (MSDANet) for medical image segmentation. Specifically, it is mainly composed of a parallel dilation pooling module (PD), a multi-scale channel attention mechanism (MSCA), a multilayer perceptron squeeze and excitation module (MSE), a big kernel convolution module (BC), and a skip feature pyramid module (SFP). The proposed PD reduces the loss of subtle features during downsampling. In order to improve the extraction of effective features, MSCA, MSE and BC are used in encoding and decoding stages. In the skip connection stage, SPF is used to reduce the difference between the low-level and high-level semantic information to accelerate the network learning. We validate the proposed model on two publicly available medical image datasets. The results show that MSDANet has superior performance on several evaluation metrics compared to state-of-the-art methods. Code is available at https://github.com/1999luan/MSDANet. 

MSDANet: A multi-scale dilation attention network for medical image segmentation

Chest Computed tomography (CT) scans detect lung nodules and assess pulmonary fibrosis. While pulmonary fibrosis indicates increased lung cancer risk, current clinical practice characterizes nodule risk of malignancy based on nodule size and smoking history; little consideration is given to the fibrotic microenvironment.To evaluate the effect of incorporating fibrotic microenvironment into classifying malignancy of lung nodules in chest CT images using deep learning techniques.We developed a visualizable 3D classification model trained with in-house CT dataset for the nodule malignancy classification task. Three slightly-modified datasets were created: (1) nodule alone (microenvironment removed); (2) nodule with surrounding lung microenvironment; and (3) nodule in microenvironment with semantic fibrosis metadata. For each of the models, tenfold cross-validation was performed. Results were evaluated using quantitative measures, such as accuracy, sensitivity, specificity, and area-under-curve (AUC), as well as qualitative assessments, such as attention maps and class activation maps (CAM).The classification model trained with nodule alone achieved 75.61% accuracy, 50.00% sensitivity, 88.46% specificity, and 0.78 AUC; the model trained with nodule and microenvironment achieved 79.03% accuracy, 65.46% sensitivity, 85.86% specificity, and 0.84 AUC. The model trained with additional semantic fibrosis metadata achieved 80.84% accuracy, 74.67% sensitivity, 84.95% specificity, and 0.89 AUC. Our visual evaluation of attention maps and CAM suggested that both the nodules and the microenvironment contributed to the task.The nodule malignancy classification performance was found to be improving with microenvironment data. Further improvement was found when incorporating semantic fibrosis information. 

Lung nodule malignancy classification with associated pulmonary fibrosis using 3D attention-gated convolutional network with CT scans

Medical image segmentation is a fundamental research problem in the field of medical image processing. Recently, the Transformer have achieved highly competitive performance in computer vision. Therefore, many methods combining Transformer with convolutional neural networks (CNNs) have emerged for segmenting medical images. However, these methods cannot effectively capture the multi-scale features in medical images, even though texture and contextual information embedded in the multi-scale features are extremely beneficial for segmentation. To alleviate this limitation, we propose a novel Transformer–CNN combined network using multi-scale feature learning for three-dimensional (3D) medical image segmentation, which is called MS-TCNet. The proposed model utilizes a shunted Transformer and CNN to construct an encoder and pyramid decoder, allowing six different scale levels of feature learning. It captures multi-scale features with refinement at each scale level. Additionally, we propose a novel lightweight multi-scale feature fusion (MSFF) module that can fully fuse the different-scale semantic features generated by the pyramid decoder for each segmentation class, resulting in a more accurate segmentation output. We conducted experiments on three widely used 3D medical image segmentation datasets. The experimental results indicated that our method outperformed state-of-the-art medical image segmentation methods, suggesting its effectiveness, robustness, and superiority. Meanwhile, our model has a smaller number of parameters and lower computational complexity than conventional 3D segmentation networks. The results confirmed that the model is capable of effective multi-scale feature learning and that the learned multi-scale features are useful for improving segmentation performance. We open-sourced our code, which can be found at https://github.com/AustinYuAo/MS-TCNet. 

MS-TCNet: An effective Transformer-CNN combined network using multi-scale feature learning for 3D medical image segmentation

Medical image segmentation aims at recognizing the object of interest from surrounding tissues and structures, which is essential for the reliable diagnosis and morphological analysis of specific lesions. Automatic medical image segmentation has been significantly boosted by deep Convolutional Neural Networks (CNNs). However, CNNs usually fail to model long-range interactions due to the intrinsic locality of convolutional operations, which limits the segmentation performance. Recently, Transformer has been successfully applied in various computer visions, which leverages the self-attention mechanism for modelling long-range interactions to capture global information. Nevertheless, self-attention suffers from lacks of spatial locality and efficient computation. To address these issues, in this work, we develop a new sparse medical Transformer (SMTF) with multiscale contextual fusion for medical image segmentation. The proposed model combines convolutional operations and attention mechanisms to form a U-shaped framework to capture both local and global information. Specifically, to reduce the computational cost of traditional Transformer, we design a novel sparse attention module to construct Transformer layers by spherical Locality Sensitive Hashing method. The sparse attention partitions the feature space into different attention buckets, and the attention calculation is conducted only in the individual bucket. The designed sparse Transformer layer further incorporates a bottleneck block to construct the encoder in SMTF. It is worth noting that the proposed sparse Transformer can also aggregate the global feature information in early stages, which enables the model to learn more local and global information by incorporating CNNs at lower layers. Furthermore, we introduce a deep supervision strategy to guide the model to fuse multiscale feature information. It further enables the SMTF to effectively propagate feature information across layers to preserve more input spatial information and mitigate information attenuation. Benefiting from these, it can achieve better segmentation performance while being more robust and efficient. The proposed SMTF is evaluated on multiple medical image segmentation datasets and a clinical nasopharyngeal carcinoma dataset. Extensive experiments have demonstrated its superiority on both qualitative and quantitative evaluations. Code and models are available at https://github.com/qmx717/sparse-attention.git. 

SMTF: Sparse transformer with multiscale contextual fusion for medical image segmentation

AMSUnet: A neural network using atrous multi-scale convolution for medical image segmentation

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AMSUnet: A neural network using atrous multi-scale convolution for medical image segmentation