Mr. Devansh Barot Profile

Devansh Barot

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Publications (1)

Proceedings Article | 2 April 2024 Presentation + Paper

3D spatial and channel reconstruction for large image patch based segmentation of knee anatomical structures on MRI

Zeqiu Yu, Xiaoyan Zhao, Jatin Singh, Jing Wang, Tong Yu, Devansh Barot, Jiantao Pu

Proceedings Volume 12926, 129260X (2024) https://doi.org/10.1117/12.3006799

KEYWORDS: Image segmentation, Ablation, Convolution, Magnetic resonance imaging, Feature fusion, Anatomy, 3D image processing, Education and training, 3D image reconstruction, Performance modeling

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Purpose: The complexity of three-dimensional (3D) image segmentation exceeds that of its 2D counterpart, primarily due to increased memory demands resulting from the larger size of input images. To mitigate these challenges, a common strategy is to extract patches from the original preprocessed images. The use of sophisticated models can sometimes offset the trade-off associated with input image size, but increased image sizes tend to yield superior performance. In this study, we aim to alleviate memory constraints while improving the accuracy of segmenting 9 distinct substructures within the knee anatomy as compared to state-of-the-art models.
Approach: We utilized a cohort of 183 knee MRI scans, with nine anatomical structures annotated by radiologists. This dataset was divided into training, validation, and independent testing sets at a ratio of 8:1:1, respectively. The second standard convolutional layer of the nnU-Net is substituted with our MS-SCConv module at each down-sampling stage. The MS-SCConv module employs soft attention mechanisms to weight both the original and refined feature maps. The refined feature maps are produced using the SCConv process, which integrates two units specifically designed to minimize feature redundancy from both spatial and channel perspectives. Comprehensive ablation studies were performed to evaluate the specific contributions of the MS-SCConv module within the informative nnU-Net framework. The performance of our proposed model was compared with several other models, including U-Net, UNETR, DynU-Net and nnU-Net.
Results: Informative nnU-Net demonstrated superior segmentation performance, achieving the high Dice coefficient (0.83) compared to other models, namely U-Net (0.71), UNETR (0.71), DynU-Net (0.74), and nnU-Net (0.81) (p<0.05). Notably, Informative nnU-Net achieves improved accuracy while reducing the number of parameters from 27.2M to 26.9M.
Conclusions: Our experiments revealed that with MS-SCConv and informative nnU-Net, the number of parameters can be decreased while improving segmentation performance.

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