Accurate volumetric segmentation of brain tumors and tissues is beneficial for quantitative brain analysis and brain disease identification in multi-modal Magnetic Resonance (MR) images. Nevertheless, due to the complex relationship between modalities, 3D Fully Convolutional Networks (3D FCNs) using simple multi-modal fusion strategies hardly learn the complex and nonlinear complementary information between modalities. Meanwhile, the indiscriminative feature aggregation between low-level and high-level features easily causes volumetric feature misalignment in 3D FCNs. On the other hand, the 3D convolution operations of 3D FCNs are excellent at modeling local relations but typically inefficient at capturing global relations between distant regions in volumetric images. To tackle these issues, we propose an Aligned Cross-Modality Interaction Network (ACMINet) for segmenting the regions of brain tumors and tissues from MR images. In this network, the cross-modality feature interaction module is first designed to adaptively and efficiently fuse and refine multi-modal features. Secondly, the volumetric feature alignment module is developed for dynamically aligning low-level and high-level features by the learnable volumetric feature deformation field. Thirdly, we propose the volumetric dual interaction graph reasoning module for graph-based global context modeling in spatial and channel dimensions. Our proposed method is applied to brain glioma, vestibular schwannoma, and brain tissue segmentation tasks, and we performed extensive experiments on BraTS2018, BraTS2020, Vestibular Schwannoma, and iSeg-2017 datasets. Experimental results show that ACMINet achieves state-of-the-art segmentation performance on all four benchmark datasets and obtains the highest DSC score of hard-segmented enhanced tumor region on the validation leaderboard of the BraTS2020 challenge.

Objectives: To investigate the prediction of pathologic complete response (pCR) in patients with non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunochemotherapy (NAIC) using quantification of intratumoral heterogeneity from pre-treatment CT image.Methods: This retrospective study included 178 patients with NSCLC who underwent NAIC at 4 different centers. The training set comprised 108 patients from center A, while the external validation set consisted of 70 patients from center B, center C, and center D. The traditional radiomics model was contrasted using radiomics features. The radiomics features of each pixel within the tumor region of interest (ROI) were extracted. The optimal division of tumor subregions was determined using the K-means unsupervised clustering method. The internal tumor heterogeneity habitat model was developed using the habitats features from each tumor sub-region. The LR algorithm was employed in this study to construct a machine learning prediction model. The diagnostic performance of the model was evaluated using criteria such as area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV).Results: In the training cohort, the traditional radiomics model achieved an AUC of 0.778 [95% confidence interval (CI): 0.688-0.868], while the tumor internal heterogeneity habitat model achieved an AUC of 0.861 (95% CI: 0.789-0.932). The tumor internal heterogeneity habitat model exhibits a higher AUC value. It demonstrates an accuracy of 0.815, surpassing the accuracy of 0.685 achieved by traditional radiomics models. In the external validation cohort, the AUC values of the two models were 0.723 (CI: 0.591-0.855) and 0.781 (95% CI: 0.673-0.889), respectively. The habitat model continues to exhibit higher AUC values. In terms of accuracy evaluation, the tumor heterogeneity habitat model outperforms the traditional radiomics model, achieving a score of 0.743 compared to 0.686.Conclusion: The quantitative analysis of intratumoral heterogeneity using CT to predict pCR in NSCLC patients undergoing NAIC holds the potential to inform clinical decision-making for resectable NSCLC patients, prevent overtreatment, and enable personalized and precise cancer management.

Brain tissue segmentation in multi-modal magnetic resonance (MR) images is significant for the clinical diagnosis of brain diseases. Due to blurred boundaries, low contrast, and intricate anatomical relationships between brain tissue regions, automatic brain tissue segmentation without prior knowledge is still challenging. This paper presents a novel 3D fully convolutional network (FCN) for brain tissue segmentation, called APRNet. In this network, we first propose a 3D anisotropic pyramidal convolutional reversible residual sequence (3DAPC-RRS) module to integrate the intra-slice information with the inter-slice information without significant memory consumption; secondly, we design a multi-modal cross-dimension attention (MCDA) module to automatically capture the effective information in each dimension of multi-modal images; then, we apply 3DAPC-RRS modules and MCDA modules to a 3D FCN with multiple encoded streams and one decoded stream for constituting the overall architecture of APRNet. We evaluated APRNet on two benchmark challenges, namely MRBrainS13 and iSeg-2017. The experimental results show that APRNet yields state-of-the-art segmentation results on both benchmark challenge datasets and achieves the best segmentation performance on the cerebrospinal fluid region. Compared with other methods, our proposed approach exploits the complementary information of different modalities to segment brain tissue regions in both adult and infant MR images, and it achieves the average Dice coefficient of 87.22% and 93.03% on the MRBrainS13 and iSeg-2017 testing data, respectively. The proposed method is beneficial for quantitative brain analysis in the clinical study, and our code is made publicly available.