Interactive Machine Learning-Based Multi-Label Segmentation of Solid Tumors and Organs

Dual-branch feature fusion S3D V-Net network for lung nodules segmentation

BACKGROUND

Accurate segmentation of lung nodules can help doctors get more accurate results and protocols in early lung cancer diagnosis and treatment planning, so that patients can be better detected and treated at an early stage, and the mortality rate of lung cancer can be reduced.

PURPOSE

Currently, the improvement of lung nodule segmentation accuracy has been limited by his heterogeneous performance in the lungs, the imbalance between segmentation targets and background pixels, and other factors. We propose a new 2.5D lung nodule segmentation network model for lung nodule segmentation. This network model can well improve the extraction of edge information of lung nodules, and fuses intra-slice and inter-slice features, which makes good use of the three-dimensional structural information of lung nodules and can more effectively improve the accuracy of lung nodule segmentation.

METHODS

Our approach is based on a typical encoding-decoding network structure for improvement. The improved model captures the features of multiple nodules in both 3-D and 2-D CT images, complements the information of the segmentation target's features and enhances the texture features at the edges of the pulmonary nodules through the dual-branch feature fusion module (DFFM) and the reverse attention context module (RACM), and employs central pooling instead of the maximal pooling operation, which is used to preserve the features around the target and to eliminate the edge-irrelevant features, to further improve the performance of the segmentation of the pulmonary nodules.

RESULTS

We evaluated this method on a wide range of 1186 nodules from the LUNA16 dataset, and averaging the results of ten cross-validated, the proposed method achieved the mean dice similarity coefficient (mDSC) of 84.57%, the mean overlapping error (mOE) of 18.73% and average processing of a case is about 2.07 s. Moreover, our results were compared with inter-radiologist agreement on the LUNA16 dataset, and the average difference was 0.74%.

CONCLUSION

The experimental results show that our method improves the accuracy of pulmonary nodules segmentation and also takes less time than more 3-D segmentation methods in terms of time.

The Uroprotective Efficacy of Total Ginsenosides in Chinese Ginseng on Chemotherapy with Cyclophosphamide

Hemorrhagic cystitis is a recognizable complication of cyclophosphamide (CYP) attributable to its lively metabolite acrolein, which produces urothelial injury. The study intended to examine the uroprotective efficacy of total ginsenosides in Chinese ginseng (TGCG) in CYP-induced hemorrhagic cystitis. In total, 24 virgin female rats were randomized into four groups as follows: group 1 (control group; injected with normal saline), group 2 (injected with CYP plus a placebo with normal saline), group 3 (given CYP and TGCG (200 mg/kg)), and group 4 (given CYP and 2-mercaptoethane sulfonate sodium (Mesna, 30 mg/kg)). An evaluation by cystometry was conducted. Values of the voiding interval were assessed in anesthetized rats and histological examinations of the bladders were measured. In the cystometry analysis, the voiding interval was significantly reduced in the CYP group. TGCG and Mesna significantly increased in the voiding interval values, individually. Bladder edema and urothelial injury were examined after contact with CYP. Contrasted to the group given CYP, CYP-induced hemorrhagic cystitis, TGCG significantly increased the urothelial thickness, and significantly reduced scores of mucosal break and submucosal edema in the bladder. In conclusion, these findings mean that the treatment with TGCG in CYP rats can avoid hemorrhagic cystitis. TGCG decreases urothelial injury. TGCG may participate as the chief character of uroprotection in CYP-induced hemorrhagic cystitis.

The value of AI in the Diagnosis, Treatment, and Prognosis of Malignant Lung Cancer

Malignant tumors is a serious public health threat. Among them, lung cancer, which has the highest fatality rate globally, has significantly endangered human health. With the development of artificial intelligence (AI) and its integration with medicine, AI research in malignant lung tumors has become critical. This article reviews the value of CAD, computer neural network deep learning, radiomics, molecular biomarkers, and digital pathology for the diagnosis, treatment, and prognosis of malignant lung tumors.