Objective

2

Hippocampus is a structure in the brain in memory consolidation and can be divided into nine subfields. Hippocampus atrophy has been mostly studied in various neurological diseases

such as Alzheimer's disease and mild cognitive impairment. Accurate hippocampus subfield segmentation in magnetic resonance (MR) images plays a crucial role in the diagnosis

prevention

and treatment of neurological diseases. However

the segmentation is a challenging task due to small size

relatively low contrast

complex shape

and indistinct boundaries of hippocampus subfields. Numerous scholars have been engaged in hippocampus subfield segmentation. Multi-atlas-based methods can obtain accurate segmentation results by fusing propagated labels of multiple atlases in a target image space. However

the performance of multi-atlas significantly relies on the effectiveness of the label fusion method. Deep learning algorithms have emerged as promising machine-learning tools in general imaging and computer vision domains

such as medical image segmentation. However

Cornu Ammonis (CA)2 and CA3 are limited by MR resolution and are thus significantly smaller than other subfields in hippocampus MR images. Most deep learning algorithms with identical network models and uniform patches present poor segmentation accuracy regardless of the considerable differences in the volume of different subfields.

Method

2

This study proposes a combined multi-scale patch and cascaded convolutional neural network (CNN)-based classification algorithm for segmenting the hippocampus into nine subfields to address the aforementioned deficiencies. In comparison with traditional label fusion method

the proposed method does not rely on explicit features but learns to extract important features for classification. Two different CNNs are designed considering the significant volume differences among different subfields. Network 1

which considers large patches as inputs

is trained to segment large subfields accurately. Network 2

which includes two patch types with small sizes that form a two-pathway network

is trained to obtain high segmentation accuracy on small subfields. Each network is trained using datasets from multiple atlases. The same number of patches is randomly extracted from different subfields that comprise a balanced training set to manage imbalanced datasets

in which the training patches of CA2 and CA3 are less than those of other subfields. The segmentation is performed slice by slice along the coronal direction

and a two-phase cascaded segmentation procedure is designed. First

the preliminary segmentation is performed using Network 1. Second

the voxels in small subfields are further classified using Network 2. The prior structural knowledge is used to recognize and correct mislabeling voxels to further improve the segmentation accuracy.

Result

2

All experiments are validated on CIND dataset

which contains 32 subjects with manually labeled ROIs. After the preprocessing

three separate training sets

which consist of patches with three different sizes

are extracted from multiple atlases. The training data of different classes are numerically balanced. We investigate the influence of using different sizes of input patches and kernels at the first convolutional layer of Network 1 to select the most appropriate parameters. We evaluate the different segmentation performances on small subfields by using the two different networks. The Dice similarity coefficient is selected as the evaluation metric. Several approaches in recent literature are implemented to compare with the proposed algorithm. Quantitative and qualitative comparisons demonstrate that the proposed method outperforms the traditional label fusion method on most subfields. The accuracies on tail

SUB

and PHG subfields are 0.865

0.81

and 0.773

respectively. On the two small subfields

CA2 and CA3

the proposed method exhibits better performances with accuracies of 60% and 64%

surpassing the traditional method by 6% and 9%

respectively.

Conclusion

2

In this paper

we propose a combined multi-scale patch and cascaded CNN-based method for hippocampus subfield segmentation. Two different networks are developed to solve the problems related to significant volume differences among different subfields. Three different sizes are used as inputs of two networks to capture considerable contextual information. A balanced training set is established to avoid incorrect training of the networks. We describe a cascaded segmentation procedure using the proposed networks. This cascaded procedure integrates two different networks for accurate segmentation on different subfields. The experiments show that the two CNNs with nonuniform patches outperform an identical network with a uniform patch. The significant improvement compared with traditional method shows that the feature extracted by the proposed network is considerably more effective and distinctive. Therefore

the proposed algorithm can label with high accuracy and is highly appropriate for hippocampus subfield segmentation.