Advancements in Brain Tumor Segmentation Techniques

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Introduction

Detection of the lesion, which in the case of brain tumors is often referred to as segmentation is essential in neuroimaging for diagnoses, therapy planning, and assessment of the progress of the illness, especially gliomas, which are some of the most aggressive and prevalent kinds of brain tumors. In the last couple of decades, improvements in the methods for tumor segmentation especially in the brain, have been accomplished based on MRI. These have evolved from previously used traditional methods of segmentation to more advanced forms of automation-propelled segmentation through the application of machine learning and AI. The development of those techniques not only enhanced the precision and speed of tumor segmentation but also proved promising in terms of its reflection on the patient’s outcome. This article reviews the recent literature and describes the state of the art in the BID approach, various techniques of Brain tumor segmentation, and their future scopes.

Evolution of Brain Tumor Segmentation Techniques

Brain tumor segmentation was initially performed manually, which the radiologists and neurosurgeons used to segment areas of the tumor on the MRI images. Although M mode proved to be the most accurate way of segmenting the images, it was also time-consuming, cumbersome, and experienced large inter-observer variability. With that realization that patterns could not hold up to newer, more stringent tests for reliability and reproducibility, new semi-automated techniques were designed with the incorporation of edge detection and thresholding techniques. These methods, however, were semi-automatic and demanded much human interaction and contravariance in the appearance of the tumor in different patients.

The following big steps forward were connected with the popularization of automated segmentation methods. These methods employed statistical models and machine learning algorithms to analyze tumor shapes with as little input from human operators as possible. From them, Gaussian mixture models and Markov random fields are often used because the shapes and intensities of tumors are usually variable. However, these methods have their drawbacks when it comes to segmenting tumors into three different types of regions, taking into account the heterogeneity of the tumors in question, such as glioblastomas.

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Machine Learning and AI in Brain Tumor Segmentation

With the help of machine learning, especially deep learning, the problem of segmenting brain tumors has undergone some dynamic changes. CNNs have been leading this revolution for the past few years and remarkably accurately segmenting tumors from MRI scans. CNNs are especially good at identifying spatial hierarchies in images and therefore are well-suited for the challenges of segmentation of brain tumors. Compared with the manually designed features of the traditional approaches, CNNs can learn all the features by themselves, which leads to a higher precision and robustness of the segmentation.

There are many discoveries made in this area, one of which is the employment of multi-modal information in segmentation. The malignant brain tumors have a complex structure and could have different characteristics in different regions of the lesion (like the necrotic core, the enhancing tumor, and the edema), which could be seen with different contrasts in MRI sequences (as T1, T2, and FLAIR images). These different sequences are employed in multi-modal approaches that present the tumor from a broader perspective; hence, a more accurate segmentation is produced. Methods such as deep learning models like U-Net and its variants have been developed for multi-modal datasets to give very high accuracy in segmentation.

One of the most important innovations reported is the combination of probabilistic models with deep learning. That is why these hybrid models, which combine the positive features of the probabilistic models (including uncertainty estimates) and the possibilities of the deep learning approaches (providing high accuracy), can present segmentation results of higher reliability. For example, constructing uncertainty into their predictions, Bayesian neural networks have been tested in the identification of brain tumors and segmentation of the same by not only presenting different segmentation results but also the uncertainty map of those particular images.

Among the methods that were developed during the last years, atlas-based segmentation can be distinguished. This method involves the placement of annotations or ‘landmarks’ of a pre-segmented atlas (which is like a template brain) on a patient’s MRI scan. The atlas is then warped to consider the patient’s morphology, and the tumor is detected based on the result of such a warping. Atlas-based methods are particularly important in those cases when the tumor distorts the anatomy significantly, for instance in glioblastomas. These methods have been improved by employing the machine learning technique, wherein during the deformation process the model is given probabilities of the tumor-related changes in the anatomy of the brain.

Another technique is based on the graph level and can be used for the segmentation of tumors. With this approach, the image is represented as a graph, with its nodes being the pixels or regions of an image, which have the edges between them reflecting their degree of similarity. In the segmentation process, it is crucial to find a cut in this graph that will separate the tumor from the rest of the brain. Both of these approaches are especially useful in the management of spatial topology between different regions of the tumor and the surrounding brain. Graph-based methods combined with deep learning have improved the results even more, and thus they can be used to segment brain tumors.

To the rest of these, there is growing literature on the analysis of brain tumors and in the development of methods that can cater to the longitudinal analysis of the disease. Historical review means that the morphology of a tumor is evaluated over time, which is important in monitoring the effects of treatment in cancer management. The standard approaches to segmentation fail at this step because the appearance of the tumor significantly differs between time points. But what has emerged recently are the 4D segmentation techniques that input the temporal information to it and thereby help to trace the tumor changes more consistently and accurately.

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Challenges and Future Directions

Nevertheless, some issues are still to be encountered when it comes to the segmentation of brain tumors: By far the greatest obstacle, however, is the fact that tumors can look different in different patients and can also change over time in the same patient. This variability is partly the reason why it becomes easy to model datasets with little transfer from one set to another. Further, there are not many big annotated data sets that can be used to train deep learning algorithms to improve segmentation methods. Even though challenges like Brain Tumor Image Segmentation (BraTS) have released significant datasets out there, the models still lack proper and diverse data.

Another issue that is also noteworthy is the application of these segregation methods in clinical work. Even though the ADV of automated segmentation methods has been promising in research settings, their implementation in clinical settings has been less. This is partly due to the obligatory validation and the necessity of these methods to integrate into clinical practice. In addition, what has been a major issue with deep learning models is their interpretability. There is also the need to build clinician’s confidence and trust in the decisions made by these models. which can be done by developing Explainable Artificial Intelligence (XAI) techniques that can help clinicians understand why a particular segmentation decision was made.

As for future work, brain tumor segmentation will further develop based on the eloquent combination of AI and nerves and neuroradiology expertise. To address the current issues, one can expect the emergence of models that incorporate the best features of several approaches to segmentation. Moreover, the idea of federative learning, when the models are trained using the data of different institutions but the data itself is not shared, may help to overcome the problem of the lack of data while maintaining the patient’s confidentiality. These technologies still have the potential to not only enhance the features of the segmentation of brain tumors but also have the potential to change how brain tumors are diagnosed and treated.

Conclusion

The area of interest for brain tumor segmentation has evolved significantly in the past few decades due to the spur in machine learning and AI technologies. This paper presents a field that has experienced stunning advancement and development of both manual and automatic approaches to segmenting tumors, ranging from simple omission methods to complex automated techniques that have enhanced precision, consistency, and reliability in segmenting tumors. Although some limitations need to be addressed, the progress of the integration of imaging strains and deep learning models into oncology, as well as the integration of oncologists’ knowledge and expertise into the models, provides an optimistic view of the progress of improving brain tumor segmentation and helping patients as many times as possible. Thus, as these technologies advance and are further incorporated into practice in the treatment of this disease, these technologies can further transform the diagnosis and treatment of primary brain tumors.

References

  1. Porz, N., Bauer, S., Pica, A., Schucht, P., Beck, J., Verma, R.K., Slotboom, J., Reyes, M. and Wiest, R., 2014. Multi-modal glioblastoma segmentation: man versus machine. PloS one, 9(5), p.e96873.
  2. Bauer, S., Wiest, R., Nolte, L.P. and Reyes, M., 2013. A survey of MRI-based medical image analysis for brain tumor studies. Physics in Medicine & Biology, 58(13), p.R97.
  3. Gooya, A., Pohl, K.M., Bilello, M., Cirillo, L., Biros, G., Melhem, E.R. and Davatzikos, C., 2012. GLISTR: glioma image segmentation and registration. IEEE transactions on medical imaging, 31(10), pp.1941-1954.
  4. Hamamci, A., Kucuk, N., Karaman, K., Engin, K. and Unal, G., 2011. Tumor-cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications. IEEE transactions on medical imaging, 31(3), pp.790-804.
  5. Shin, H.C., Orton, M.R., Collins, D.J., Doran, S.J. and Leach, M.O., 2012. Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data. IEEE transactions on pattern analysis and machine intelligence, 35(8), pp.1930-1943.
  6. Artaechevarria, X., Munoz-Barrutia, A. and Ortiz-de-Solorzano, C., 2009. Combination strategies in multi-atlas image segmentation: application to brain MR data. IEEE transactions on medical imaging, 28(8), pp.1266-1277.
  7. Shin, H.C., Orton, M., Collins, D.J., Doran, S. and Leach, M.O., 2011, December. Autoencoder in time-series analysis for unsupervised tissues characterisation in a large unlabelled medical image dataset. In 2011 10th international conference on machine learning and applications and workshops (Vol. 1, pp. 259-264). IEEE.

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