Rarasmaya Indraswari, - and Takio Kurita, - and Agus Zainal Arifin, - and Nanik Suciati, - and Eha Renwi Astuti, - (2019) Multi-projection deep learning network for segmentation of 3D medical images. Pattern Recognition Letters, 125 (1). pp. 791-797. ISSN 0167-8655

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Abstract

Segmentation of three-dimensional (3D) medical images using deep learning is a challenging task due to the lack of a 3D medical image dataset and their ground truth, resource memory limitations, and imbal- anced dataset problem. In this paper, we propose advanced deep learning network for segmentation of 3D medical images. The proposed Multi-projection Network can preserve resource memory by applying two-dimensional (2D) kernels while still obtaining the 3D information from the image by incorporating slices from different planar projections of the 3D image to achieve good segmentation results. The pro- posed network uses a weighted cost function to address the imbalanced dataset problem and introduces an adaptive weight that considers the probability of each class in the image. The experimental results showed that the proposed Multi-projection Network can produce the highest sensitivity (true positive rate) compared to other architectures despite the high class imbalance in the dataset and small amount of training data.

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