ψ-Net: Stacking Densely Convolutional LSTMs for Sub-Cortical Brain Structure Segmentation

Lihao Liu, Xiaowei Hu, Lei Zhu, Chi Wing Fu, Jing Qin, Pheng Ann Heng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

15 Citations (Scopus)

Abstract

Sub-cortical brain structure segmentation is of great importance for diagnosing neuropsychiatric disorders. However, developing an automatic approach to segmenting sub-cortical brain structures remains very challenging due to the ambiguous boundaries, complex anatomical structures, and large variance of shapes. This paper presents a novel deep network architecture, namely \Psi -Net, for sub-cortical brain structure segmentation, aiming at selectively aggregating features and boosting the information propagation in a deep convolutional neural network (CNN). To achieve this, we first formulate a densely convolutional LSTM module (DC-LSTM) to selectively aggregate the convolutional features with the same spatial resolution at the same stage of a CNN. This helps to promote the discriminativeness of features at each CNN stage. Second, we stack multiple DC-LSTMs from the deepest stage to the shallowest stage to progressively enrich low-level feature maps with high-level context. We employ two benchmark datasets on sub-cortical brain structure segmentation, and perform various experiments to evaluate the proposed \Psi -Net. The experimental results show that our network performs favorably against the state-of-the-art methods on both benchmark datasets.

Original languageEnglish
Article number9007625
Pages (from-to)2806-2817
Number of pages12
JournalIEEE Transactions on Medical Imaging
Volume39
Issue number9
DOIs
Publication statusPublished - Sep 2020

Keywords

  • densely convolutional LSTM
  • feature integration
  • Sub-cortical brain structure segmentation

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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