Medical Image Segmentation using AI

 Medical Image Segmentation using AI

Malina Diaconescu, Adrian Pal 

Introduction

Medical image segmentation has been very challenging due to the large variation of anatomy across different cases. Recent advances in deep learning frameworks have exhibited faster and more accurate performance in image segmentation. 

Medical images play a key role in medical treatment and diagnosis. The goal of Computer-Aided Diagnosis (CAD) systems is providing doctors with more precise interpretation of medical images to follow-up of many diseases and have better treatment of a large number of patients. Moreover, accurate and reliable processing of medical images results in reducing the time, cost, and error of human-based processing. A critical step in numerous medical imaging studies is image segmentation. Medical image segmentation is the process of partitioning an image into multiple meaningful regions. Due to the complex geometry and inherent noise value of medical images, segmentation of these images is difficult. 

Interest in medical image segmentation has grown considerably in the last few years. This is due in part to the large number of application domains, like segmentation of blood vessel, skin cancer, lung, and cell nuclei.

 Different applications of medical image segmentation

Real Life Application

For instance, segmentation of blood vessels will help to detect and treat many diseases that influence the blood vessels. Width and curves of retinal blood vessel show some symptoms about many diseases. Early diagnosis of many sight threatening diseases is vital since lots of these diseases like glaucoma, hypertension and diabetic retinopathy cause blindness among working age people. Skin lesion segmentation helps to detect and diagnosis the skin cancer in the early stage. One of the most deadly form of skin cancer is melanoma, which is the result of unusual growth of melanocytes. Dermoscopy, captured by the light magnifying device and immersion fluid, is a non-invasive imaging technique providing with a visualization of the skin surface. The detection of melanoma in dermoscopic images by the dermatologists may be inaccurate or subjective. If melanoma is detected in its early stages, the five-year relative survival rate is 92%, also explained in [1].

The first vital step of pulmonary image analysis is identifying the boundaries of lung from surrounding thoracic tissue on CT images, called lung segmentation. It can also be applied to lung cancer segmentation. Another application of medical image segmentation is cell nuclei segmentation. All known biological lives include a fundamental unit called cell. By segmentation of nuclei in different situations, we can understand the role and function of the nucleus and the DNA contained in cell in various treatments.

Technical Discussion

Deep learning networks achieve outstanding results and use to outperform non-deep state-of-the-art methods in medical imaging. These networks require a large amount of data to train and provide a good generalization behavior given the huge number of network parameters. A critical issue in medical image segmentation is the unavailability of large (and annotated) datasets. In medical image segmentation, per pixel labeling is required instead of image level label. Fully convolutional neural network (presented also in [2]) was one of the first deep networks applied to image segmentation.

In his research paper at [3]. Ronneberger  extended this architecture to U-Net, achieving good segmentation results leveraging the need of a large amount of training data. Their network consists of encoding and decoding paths. In the encoding path a large number of feature maps with reduced dimensionality are extracted. The decoding path is used to produce segmentation maps (with the same size as the input) by performing upconvolutions. Many extensions of U-Net have been proposed so far in [4]–[6]. In some of them, the extracted feature maps in the skip connection are first fed to a processing step (e.g. attention gates, explained in [5]) and then concatenated. The main drawback of these networks is that the processing step is performed individually for the two sets of feature maps, and these features are then simply concatenated.

In this paper, we propose Multi-level Context Gating U-Net (MCGU-Net) an extended version of the U-Net, by including BConvLSTM presented in [7] in the skip connection, using SE mechanism in the decoding path, and reusing feature maps with densely convolutions. A VGG backbone is employed in the encoding path to make it possible to use pre-trained weights on large datasets. The feature maps from the corresponding encoding layer have higher resolution while the feature maps extracted from the previous up-convolutional layer contain more semantic information. Instead of a simple concatenation, combining these two kinds of features with non-linear functions in all levels of the network may result in more precise segmentation. Therefore, in this paper we extend the U-Net architecture by adding multi-level BConvLSTM in the skip connection.

Experimental Results

Segmentation Results on Blood Vessels Dataset

Segmentation Results on 3 different Skin Datatsets

Segmentation Results on Lung Dataset

Segmentation Results on Cell Nuclei Dataset

Conclusions 

Future research in the segmentation of medical images will strive towards improving the accuracy, precision, and computational speed of segmentation methods, as well as reducing the amount of manual interaction. Accuracy and precision can be improved by incorporating prior information from atlases and by combining discrete and continuous-based segmentation methods. For increasing computational efficiency, multiscale processing and parallelizable methods such as neural networks appear to be promising approaches. Computational efficiency will be particularly important in real-time processing applications. Possibly the most important question surrounding the use of image segmentation is its application in clinical settings. Computerized segmentation methods have already demonstrated their utility in research applications and are now garnering increased use for computer aided diagnosis and radiotherapy planning. It is unlikely that automated segmentation methods will ever replace physicians but they will likely become crucial elements of medical image analysis. Segmentation methods will be particularly valuable in areas such as computer integrated surgery, where visualization of the anatomy is a critical component

Bibliography

1. R. L. Siegel and K. D. Miller, “Jemal a (2018) cancer statistics,” Ca Cancer J Clin, vol. 68, no. 1, pp. 7–30, 2018.
2. J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in CVPR, 2015, pp. 3431–3440.
3. O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on MICCAI. Springer, 2015, pp. 234–241. 
4. M. Z. Alom, M. Hasan, C. Yakopcic, T. M. Taha, and V. K. Asari, “Recurrent residual convolutional neural network based on u-net (r2unet) for medical image segmentation,” arXiv preprint arXiv:1802.06955, 2018.
5. O. Oktay et al., “Attention u-net: Learning where to look for the pancreas,” arXiv preprint arXiv:1804.03999, 2018.
6. R. Azad, M. Asadi-Aghbolaghi, M. Fathy, and S. Escalera, “Bidirectional convlstm u-net with densley connected convolutions,” in Proceedings of the CVPRW, 2019, pp. 0–0.
7. H. Song, W. Wang, S. Zhao, J. Shen, and K.-M. Lam, “Pyramid dilated deeper convlstm for video salient object detection,” in Proceedings of the ECCV, 2018, pp. 715–731.
8. Asadi-Aghbolaghi, M., Azad, R., Fathy, M., & Escalera, S. (2020). Multi-level context gating of embedded collective knowledge for medical image segmentation. arXiv preprint arXiv:2003.05056.
9. Pham, D. L., Xu, C., & Prince, J. L. (2000). A survey of current methods in medical image segmentation. Annual review of biomedical engineering, 2(3), 315-337.