Lung CT Image Segmentation Using Deep Neural Networks

By Brahim Ait Skourt, Abdelhamid El Hassani, A. Majda. 2018

308 citations recorded by the source corpus.

Abstract Lung CT image segmentation is a necessary initial step for lung image analysis, it is a prerequisite step to provide an accurate lung CT image analysis such as lung cancer detection. In this work, we propose a lung CT image segmentation using the U-net architecture, one of the most used architectures in deep learning for image segmentation. The architecture consists of a contracting path to extract high-level information and a symmetric expanding path that recovers the information needed. This network can be trained end-to-end from very few images and outperforms many methods. Experimental results show an accurate segmentation with 0.9502 Dice-Coefficient index.

Subfields: Computer Vision and Pattern Recognition, Neurology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a8697fb2a9e0866b972e653a229b98efb5906b9c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Yudong Zhang
2. Colin P. Elkin
3. João Manuel Ribeiro da Silva Tavares
4. Kang Zhou
5. Shenghua Gao
6. Huan Fu
7. Yitian Zhao
8. Jiang Liu

In Neurology

1. Colin P. Elkin
2. Shenghua Gao
3. V. Devabhaktuni
4. Sidike Paheding
5. Nahian Alam Siddique

In Radiology, Nuclear Medicine and Imaging

1. Huan Fu
2. Jiang Liu
3. Jun Cheng
4. Colin P. Elkin
5. João Manuel Ribeiro da Silva Tavares
6. V. Devabhaktuni
7. Sidike Paheding
8. Nahian Alam Siddique

Citations in the corpus

• CE-Net: Context Encoder Network for 2D Medical Image Segmentation (2019, methodology): “The U-Net is also applied to directly segment lung from CT images [45].”
• Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks (2020, unknown): “Diagnosis of cardiac arrhythmia [13,14] , brain injuries [15,16] , lung segmentation [17,18] , breast cancer [19,20] , skin cancer [21,22] , epilepsy [23,40,72] , and pneumonia [24-29] with deep learning models has increased the popularity of these algorithms in biomedical field.”
• U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications (2020, background): “As with MRI, cancer diagnosis is a major that involves the application of CT imaging; including liver cancer [22], [23], [57], [71], [73], [85], [94], [109], [194]–[196], lung cancer [24], [34], [45], [48], [70], [73], [197]–[201], bone cancer [60], and cervical cancer [202].”
• Deep-learning framework to detect lung abnormality - A study with chest X-Ray and lung CT scan images (2020, result): “Similarly, the earlier assessment on the LIDC-IDRI dataset confirms that the existing techniques offer satisfactory results during the classification of the images into malignant and benign class [23,24].”