Scalable Joint Segmentation and Registration Framework for Infant Brain Images

Enhanced U-Net for Infant Brain MRI Segmentation: A (2+1)D Convolutional Approach

BACKGROUND

Infant brain tissue segmentation from MRI data is a critical task in medical imaging, particularly challenging due to the evolving nature of tissue contrasts in the early months of life. The difficulty increases as gray matter (GM) and white matter (WM) intensities converge, making accurate segmentation challenging. This study aims to develop an improved U-net-based model to enhance the precision of automatic segmentation of cerebro-spinal fluid (CSF), GM, and WM in 10 infant brain MRIs using the iSeg-2017 dataset.

METHODS

The proposed method utilizes a U-net architecture with (2+1)Dconvolutional layers and skip connections. Preprocessing includes intensity normalization using histogram alignment to standardize MRI data across different records. The model was trained on the iSeg-2017 dataset, which comprises T1-weighted and T2-weighted MRI data from ten infant subjects. Cross-validation was performed to evaluate the model's segmentation performance.

RESULTS

The model achieved an average accuracy of 92.2%, improving on previous methods by 0.7%. Sensitivity, precision, and Dice similarity scores were used to evaluate the performance, showing high levels of accuracy across different tissue types. The model demonstrated a slight bias toward misclassifying GM and WM, indicating areas for potential improvement.

CONCLUSIONS

The results suggest that the U-net architecture is highly effective in segmenting infant brain tissues from MRI data. Future work will explore enhancements such as attention mechanisms and dual-network processing for further improving segmentation accuracy.

A Novel Registration Framework for Aligning Longitudinal Infant Brain Tensor Images

Registering longitudinal infant brain images is challenging, as the infant brain undergoes rapid changes in size, shape and tissue contrast in the first months and years of life. Diffusion tensor images (DTI) have relatively consistent tissue properties over the course of infancy compared to commonly used T1 or T2-weighted images, presenting great potential for infant brain registration. Moreover, groupwise registration has been widely used in infant neuroimaging studies to reduce bias introduced by predefined atlases that may not be well representative of samples under study. To date, however, no methods have been developed for groupwise registration of tensor-based images. Here, we propose a novel registration approach to groupwise align longitudinal infant DTI images to a sample-specific common space. Longitudinal infant DTI images are first clustered into more homogenous subgroups based on image similarity using Louvain clustering. DTI scans are then aligned within each subgroup using standard tensor-based registration. The resulting images from all subgroups are then further aligned onto a sample-specific common space. Results show that our approach significantly improved registration accuracy both globally and locally compared to standard tensor-based registration and standard fractional anisotropy-based registration. Additionally, clustering based on image similarity yielded significantly higher registration accuracy compared to no clustering, but comparable registration accuracy compared to clustering based on chronological age. By registering images groupwise to reduce registration bias and capitalizing on the consistency of features in tensor maps across early infancy, our groupwise registration framework facilitates more accurate alignment of longitudinal infant brain images.

Attention-modulated multi-branch convolutional neural networks for neonatal brain tissue segmentation

Accurate measurement of brain structures is essential for the evaluation of neonatal brain growth and development. The conventional methods use manual segmentation to measure brain tissues, which is very time-consuming and inefficient. Recent deep learning achieves excellent performance in computer vision, but it is still unsatisfactory for segmenting magnetic resonance images of neonatal brains because they are immature with unique attributes. In this paper, we propose a novel attention-modulated multi-branch convolutional neural network for neonatal brain tissue segmentation. The proposed network is built on the encoder-decoder framework by introducing both multi-scale convolutions in the encoding path and multi-branch attention modules in the decoding path. Multi-scale convolutions with different kernels are used to extract rich semantic features across large receptive fields in the encoding path. Multi-branch attention modules are used to capture abundant contextual information in the decoding path for segmenting brain tissues by fusing both local features and their corresponding global dependencies. Spatial attention connections between the encoding and decoding paths are designed to increase feature propagation for both avoiding information loss during downsampling and accelerating model training convergence. The proposed network was implemented in comparison with baseline methods on three neonatal brain datasets. Our network achieves the average Dice similarity coefficients/the average Hausdorff distances of 0.9116/8.1289, 0.9367/9.8212 and 0.8931/8.1612 on the customized dCBP2021 dataset, 0.8786/11.7863, 0.8965/13.4296 and 0.8539/10.462 on the public NBAtlas dataset, as well as 0.9253/7.7968, 0.9448/9.5472 and 0.9132/7.5877 on the public dHCP2017 dataset in partitioning the brain into gray matter, white matter and cerebrospinal fluid, respectively. The experimental results show that the proposed method achieves competitive state-of-the-art performance in neonatal brain tissue segmentation. The code and pre-trained models are available at https://github.com/zhangyongqin/AMCNN.

One shot PACS: Patient specific Anatomic Context and Shape prior aware recurrent registration-segmentation of longitudinal thoracic cone beam CTs

Image-guided adaptive lung radiotherapy requires accurate tumor and organs segmentation from during treatment cone-beam CT (CBCT) images. Thoracic CBCTs are hard to segment because of low soft-tissue contrast, imaging artifacts, respiratory motion, and large treatment induced intra-thoracic anatomic changes. Hence, we developed a novel Patient-specific Anatomic Context and Shape prior or PACS-aware 3D recurrent registration-segmentation network for longitudinal thoracic CBCT segmentation. Segmentation and registration networks were concurrently trained in an end-to-end framework and implemented with convolutional long-short term memory models. The registration network was trained in an unsupervised manner using pairs of planning CT (pCT) and CBCT images and produced a progressively deformed sequence of images. The segmentation network was optimized in a one-shot setting by combining progressively deformed pCT (anatomic context) and pCT delineations (shape context) with CBCT images. Our method, one-shot PACS was significantly more accurate (p <0.001) for tumor (DSC of 0.83 ± 0.08, surface DSC [sDSC] of 0.97 ± 0.06, and Hausdorff distance at 95th percentile [HD95] of 3.97±3.02mm) and the esophagus (DSC of 0.78 ± 0.13, sDSC of 0.90±0.14, HD95 of 3.22±2.02) segmentation than multiple methods. Ablation tests and comparative experiments were also done.

Computational neuroanatomy of baby brains: A review

The first postnatal years are an exceptionally dynamic and critical period of structural, functional and connectivity development of the human brain. The increasing availability of non-invasive infant brain MR images provides unprecedented opportunities for accurate and reliable charting of dynamic early brain developmental trajectories in understanding normative and aberrant growth. However, infant brain MR images typically exhibit reduced tissue contrast (especially around 6 months of age), large within-tissue intensity variations, and regionally-heterogeneous, dynamic changes, in comparison with adult brain MR images. Consequently, the existing computational tools developed typically for adult brains are not suitable for infant brain MR image processing. To address these challenges, many infant-tailored computational methods have been proposed for computational neuroanatomy of infant brains. In this review paper, we provide a comprehensive review of the state-of-the-art computational methods for infant brain MRI processing and analysis, which have advanced our understanding of early postnatal brain development. We also summarize publically available infant-dedicated resources, including MRI datasets, computational tools, grand challenges, and brain atlases. Finally, we discuss the limitations in current research and suggest potential future research directions.

Patch-Based Label Fusion with Structured Discriminant Embedding for Hippocampus Segmentation

Automatic and accurate segmentation of hippocampal structures in medical images is of great importance in neuroscience studies. In multi-atlas based segmentation methods, to alleviate the misalignment when registering atlases to the target image, patch-based methods have been widely studied to improve the performance of label fusion. However, weights assigned to the fused labels are usually computed based on predefined features (e.g. image intensities), thus being not necessarily optimal. Due to the lack of discriminating features, the original feature space defined by image intensities may limit the description accuracy. To solve this problem, we propose a patch-based label fusion with structured discriminant embedding method to automatically segment the hippocampal structure from the target image in a voxel-wise manner. Specifically, multi-scale intensity features and texture features are first extracted from the image patch for feature representation. Margin fisher analysis (MFA) is then applied to the neighboring samples in the atlases for the target voxel, in order to learn a subspace in which the distance between intra-class samples is minimized and the distance between inter-class samples is simultaneously maximized. Finally, the k-nearest neighbor (kNN) classifier is employed in the learned subspace to determine the final label for the target voxel. In the experiments, we evaluate our proposed method by conducting hippocampus segmentation using the ADNI dataset. Both the qualitative and quantitative results show that our method outperforms the conventional multi-atlas based segmentation methods.