Judging weather there exists mediastinal lymph node metastasis in the location of mediastinal lymph node region and correctly segmenting malignant lymph nodes have great significance to the diagnosis and treatment of lung cancer. In view of the large difference in mediastinal lymph node size, the imbalance of positive and negative samples and the feature similarity between surrounding soft tissues and lung tumors, a new cascaded two-stage mediastinal lymph node segmentation algorithm based on attention was proposed. First, a two-stage segmentation algorithm was designed based on the medical prior to remove mediastinal interference tissues and then segment the suspicious lymph nodes, so as to reduce the interference of the negative samples and the difficulty of training, while enhancing the ability to segment mediastinal lymph nodes. Second, a global aggregation module and a dual attention module were introduced to improve the network's ability to classify multi-scale targets and backgrounds. Experimental results showed that the proposed algorithm achieved an accuracy of 0.707 9, a recall of 0.726 9, and a Dice score of 0.701 1 on the mediastinal lymph node dataset. It can be seen that the proposed algorithm is significantly better than other current mediastinal lymph node segmentation algorithms in terms of accuracy and Dice score, and can solve problems such as the big difference in size, sample imbalance and easily confused features of lymph nodes.

Aiming at the problem that the deep learning network model applied to medical image target detection only has a fixed receptive field and cannot effectively detect the cervical lymph nodes with obvious morphological and scale differences, a new recognition algorithm based on adaptive receptive field mechanism was proposed, applying deep learning to the automatic recognition of cervical lymph nodes in complete three-dimensional medical images at the first time. Firstly, the semi-random sampling method was used to crop the medical sequence images to generate the grid-based local image blocks and the corresponding truth labels. Then, the DeepNode network based on the adaptive receptive field mechanism was constructed and trained through the local image blocks and labels. Finally, the trained DeepNode network model was used for prediction. By inputting the whole sequence images, the cervical lymph node recognition results corresponding to the whole sequence was obtained end-to-end and quickly. On the cervical lymph node dataset, the cervical lymph node recognition using the DeepNode network has the recall rate of 98.13%, the precision of 97.38%, and the number of false positives per scan is only 29, and the time consumption is relatively shorter. The analysis of the experimental results shows that compared with current algorithms such as the combination of two-dimensional and three-dimensional convolutional neural networks, the general three-dimensional object detection and the weak supervised location based recognition, the proposed algorithm can realize the automatic recognition of cervical lymph nodes and obtain the best recognition results. The algorithm is end-to-end, simple and efficient, easy to be extended to three-dimensional target detection tasks for other medical images and can be applied to clinical diagnosis and treatment.

The existing automatic recognition algorithms for cervical lymph nodes have low efficiency, and the overall false positive removal are unsatisfied, so a cervical lymph node detection algorithm using cascaded Fully Convolutional Neural Networks (FCNs) was proposed. Firstly, combined with the prior knowledge of doctors, the cascaded FCNs were used for preliminary identification, that was, the first FCN was used for extracting the cervical lymph node region from the Computed Tomography (CT) image of head and neck. Then, the second FCN was used to extract the lymph node candidate samples from the region, and merging them at the three-dimensional (3D) level to generate a 3D image block. Finally, the proposed feature block average pooling method was introduced into the 3D classification network, and the 3D input image blocks with different scales were classified into two classes to remove false positives. On the cervical lymph node dataset, the recall of cervical lymph nodes identified by cascaded FCNs is up to 97.23%, the classification accuracy of the 3D classification network with feature block average pooling can achieve 98.7%. After removing false positives, the accuracy of final result reaches 93.26%. Experimental results show that the proposed algorithm can realize the automatic recognition of cervical lymph nodes with high recall and accuracy, which is better than the current methods reported in the literatures; it is simple and efficient, easy to extend to other tasks of 3D medical images recognition.