Multi-projection deep learning network for segmentation of 3D medical images

Rarasmaya Indraswari, T. Kurita, Agus Zainal Arifin, N. Suciati, Eha Renwi Astuti

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)


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 imbalanced 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 proposed 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.

Original languageEnglish
Pages (from-to)791-797
Number of pages7
JournalPattern Recognition Letters
Publication statusPublished - 1 Jul 2019


  • Deep learning
  • Image segmentation
  • Imbalanced dataset
  • Neural networks
  • Three-dimensional medical image


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