TY - GEN
T1 - V-Net Light - Parameter-Efficient 3-D Convolutional Neural Network for Prostate MRI Segmentation
AU - Yaniv, Ophir
AU - Portnoy, Orith
AU - Talmon, Amit
AU - Kiryati, Nahum
AU - Konen, Eli
AU - Mayer, Arnaldo
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - Prostate MRI segmentation has become an important tool for quantitative estimation of the gland volume during diagnostic imaging. It is also a critical step in the fusion between MRI and transrectal ultrasound (TRUS) for fusion guided biopsy or therapy. 3-D neural networks have demonstrated strong potential for this task, but require substantial computational resources due to their large number of parameters. In this work, we focus on the efficiency of the segmentation network in terms of speed and memory requirements. Specifically, we aim at reaching state-of-the-art results with smaller networks, involving significantly fewer parameters, thus making the network easier to train and operate. A novel 3-D network architecture, called V-net Light (VnL) is proposed, based on an efficient 3-D Module called 3-D Light, that minimizes the number of network parameters while maintaining state-of-the-art segmentation results. The proposed method is validated on the PROMISE12 challenge data [1]. The proposed VnL has only 9.1% of V-net's parameters, 3.2% of its floating point operations (FLOPs) and uses only 9.1% of hard-disk storage compared to V-net, yet V-net and VnL has comparable accuracy.
AB - Prostate MRI segmentation has become an important tool for quantitative estimation of the gland volume during diagnostic imaging. It is also a critical step in the fusion between MRI and transrectal ultrasound (TRUS) for fusion guided biopsy or therapy. 3-D neural networks have demonstrated strong potential for this task, but require substantial computational resources due to their large number of parameters. In this work, we focus on the efficiency of the segmentation network in terms of speed and memory requirements. Specifically, we aim at reaching state-of-the-art results with smaller networks, involving significantly fewer parameters, thus making the network easier to train and operate. A novel 3-D network architecture, called V-net Light (VnL) is proposed, based on an efficient 3-D Module called 3-D Light, that minimizes the number of network parameters while maintaining state-of-the-art segmentation results. The proposed method is validated on the PROMISE12 challenge data [1]. The proposed VnL has only 9.1% of V-net's parameters, 3.2% of its floating point operations (FLOPs) and uses only 9.1% of hard-disk storage compared to V-net, yet V-net and VnL has comparable accuracy.
KW - Deep learning
KW - Fast training
KW - Neural network
KW - Prostate segmentation
KW - Small model
UR - http://www.scopus.com/inward/record.url?scp=85085855498&partnerID=8YFLogxK
U2 - 10.1109/ISBI45749.2020.9098643
DO - 10.1109/ISBI45749.2020.9098643
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AN - SCOPUS:85085855498
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 442
EP - 445
BT - ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging
PB - IEEE Computer Society
T2 - 17th IEEE International Symposium on Biomedical Imaging, ISBI 2020
Y2 - 3 April 2020 through 7 April 2020
ER -