Characterization of errors in deep learning-based brain MRI segmentation

TY - CHAP

T1 - Characterization of errors in deep learning-based brain MRI segmentation

AU - Pai, Akshay Sadananda Uppinakudru

AU - Teng, Yuan-Ching

AU - Blair, Joseph Patrick Michele

AU - Kallenberg, Michiel Gijsbertus J.

AU - Dam, Erik Bjørnager

AU - Sommer, Stefan Horst

AU - Igel, Christian

AU - Nielsen, Mads

PY - 2017/1/30

Y1 - 2017/1/30

N2 - With ever-increasing data in the field of medical imaging, the availability of robust methods for quantitative analysis in large-scale studies is the need of the hour. In recent times, there has been a significant increase in the use of deep learning, in particular of convolutional neural networks (CNNs), in the field of computer vision and image analysis. In contrast to traditional shallow classifiers, deep learning methods need less domain-specific feature engineering. The architecture can automatically learn hierarchies of relevant features from raw data. Despite the many success stories from computer vision, so far there are only rather few studies on deep learning in the field of medical imaging. In this chapter, we will look more closely at a specific application of CNNs, namely segmentation of normal brains from magnetic resonance images (MRI). We will characterize the types of errors from CNN-based segmentation and compare them with the errors from a model-based registration approach. The emphasis of this chapter is on comparing errors made by model-driven and data-driven approaches. In conclusion, we notice that the two methods make complementary errors. The CNN errors can be reduced by including more training data and by finding ways to incorporate the geometric information that registration-based algorithms rely on.

AB - With ever-increasing data in the field of medical imaging, the availability of robust methods for quantitative analysis in large-scale studies is the need of the hour. In recent times, there has been a significant increase in the use of deep learning, in particular of convolutional neural networks (CNNs), in the field of computer vision and image analysis. In contrast to traditional shallow classifiers, deep learning methods need less domain-specific feature engineering. The architecture can automatically learn hierarchies of relevant features from raw data. Despite the many success stories from computer vision, so far there are only rather few studies on deep learning in the field of medical imaging. In this chapter, we will look more closely at a specific application of CNNs, namely segmentation of normal brains from magnetic resonance images (MRI). We will characterize the types of errors from CNN-based segmentation and compare them with the errors from a model-based registration approach. The emphasis of this chapter is on comparing errors made by model-driven and data-driven approaches. In conclusion, we notice that the two methods make complementary errors. The CNN errors can be reduced by including more training data and by finding ways to incorporate the geometric information that registration-based algorithms rely on.

U2 - 10.1016/B978-0-12-810408-8.00013-4

DO - 10.1016/B978-0-12-810408-8.00013-4

M3 - Book chapter

SP - 223

EP - 242

BT - Deep learning for medical image analysis

A2 - Zhou, S. Kevin

A2 - Greenspan, Hayit

A2 - Shen, Dinggang

PB - Academic Press

ER -