Radiotherapy planning for glioblastoma based on a tumor growth model: Improving target volume delineation

Glioblastoma are known to infiltrate the brain parenchyma instead of forming a solid tumor mass with a defined boundary. Only the part of the tumor with high tumor cell density can be localized through imaging directly. In contrast, brain tissue infiltrated by tumor cells at low density appears normal on current imaging modalities. In clinical practice, a uniform margin is applied to account for microscopic spread of disease. The current treatment planning procedure can potentially be improved by accounting for the anisotropy of tumor growth: Anatomical barriers such as the falx cerebri represent boundaries for migrating tumor cells. In addition, tumor cells primarily spread in white matter and infiltrate gray matter at lower rate. We investigate the use of a phenomenological tumor growth model for treatment planning. The model is based on the Fisher-Kolmogorov equation, which formalizes these growth characteristics and estimates the spatial distribution of tumor cells in normal appearing regions of the brain. The target volume for radiotherapy planning can be defined as an isoline of the simulated tumor cell density. A retrospective study involving 10 glioblastoma patients has been performed. To illustrate the main findings of the study, a detailed case study is presented for a glioblastoma located close to the falx. In this situation, the falx represents a boundary for migrating tumor cells, whereas the corpus callosum provides a route for the tumor to spread to the contralateral hemisphere. We further discuss the sensitivity of the model with respect to the input parameters. Correct segmentation of the brain appears to be the most crucial model input. We conclude that the tumor growth model provides a method to account for anisotropic growth patterns of glioblastoma, and may therefore provide a tool to make target delineation more objective and automated

3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context

We present an efficient deep learning approach for the challenging task of tumor segmentation in multisequence MR images. In recent years, Convolutional Neural Networks (CNN) have achieved state-of-the-art performances in a large variety of recognition tasks in medical imaging. Because of the considerable computational cost of CNNs, large volumes such as MRI are typically processed by subvolumes, for instance slices (axial, coronal, sagittal) or small 3D patches. In this paper we introduce a CNN-based model which efficiently combines the advantages of the short-range 3D context and the long-range 2D context. To overcome the limitations of specific choices of neural network architectures, we also propose to merge outputs of several cascaded 2D-3D models by a voxelwise voting strategy. Furthermore, we propose a network architecture in which the different MR sequences are processed by separate subnetworks in order to be more robust to the problem of missing MR sequences. Finally, a simple and efficient algorithm for training large CNN models is introduced. We evaluate our method on the public benchmark of the BRATS 2017 challenge on the task of multiclass segmentation of malignant brain tumors. Our method achieves good performances and produces accurate segmentations with median Dice scores of 0.918 (whole tumor), 0.883 (tumor core) and 0.854 (enhancing core). Our approach can be naturally applied to various tasks involving segmentation of lesions or organs.Comment: Submitted to the journal Computerized Medical Imaging and Graphic

A generative model for segmentation of tumor and organs-at-risk for radiation therapy planning of glioblastoma patients

We present a fully automated generative method for simultaneous brain tumor and organs-at-risk segmentation in multi-modal magnetic resonance images. The method combines an existing whole-brain segmentation technique with a spatial tumor prior, which uses convolutional restricted Boltzmann machines to model tumor shape. The method is not tuned to any specific imaging protocol and can simultaneously segment the gross tumor volume, peritumoral edema and healthy tissue structures relevant for radiotherapy planning. We validate the method on a manually delineated clinical data set of glioblastoma patients by comparing segmentations of gross tumor volume, brainstem and hippocampus. The preliminary results demonstrate the feasibility of the method

AUTOMATIC BRAIN TUMOR SEGMENTATION WITH K-MEANS, FUZZY C-MEANS, SELF-ORGANIZING MAP AND OTSU METHODS

AutomatIc BraIn Tumor SegmentatIon wIth K-Means, Fuzzy C-Means, Self-Organizing Map and Otsu MethodsAbstractThe human brain is an amazing organ of the human nervous system and controls all functions of our body. Brain tumors emerge from a mass of abnormal cells in the brain, and catching tumors early often allows for more treatment options. For diagnosing brain tumors, it has been benefited mostly from magnetic resonance images. In this study, we have developed the segmentation systems using the methods as K-Means, Fuzzy C-Means, Self-Organizing Map, Otsu, and the hybrid method of them, and evaluated the methods according to their success rates of segmentation. The developed systems, which take the brain image of MRI as input, perform skull stripping, preprocessing, and segmentation is performed using the clustering algorithms as K-Means, Fuzzy C-Means, Self-Organizing Map and Otsu Methods. Before preprocessing, the skull region is removed from the images in the MRI brain image data set. In preprocessing, the quality of the brain images is enhanced and the noise of the images is removed by some various filtering and morphological techniques. Finally, with the clustering and thresholding techniques, the tumor area of the brain is detected, and then the systems of the segmentation have been evaluated and compared with each other according to accuracy, true positive rate, and true negative rate.Keywords: Brain Tumor Segmentation, Medical Imaging, Fuzzy C-Means, K-Means, Self-Organizing Map, Otsu MethodBulanık C-Ortalamalar, K-Ortalamalar, Özdüzenlemelİ Ağ VE Otsu Metot İLE BEYİN TÜMÖRÜ SEGMENTASYONU Özetİnsan beyni, insan sinir sisteminin en önemli organıdır ve vücudumuzun tamamını kontrol eder. Beyin tümörleri beyindeki normal olmayan hücrelerden oluşur ve tümörleri erken tespit etmek birçok tedavi seçeneklerinin uygulanmasına olanak sağlar. Beyin tümörlerinin teşhisi için çoğunlukla manyetik rezonans görüntülerinden yararlanılmıştır. Bu çalışmada, Bulanık C-Ortalamalar, K-Ortalamalar, Özdüzenlemeli Ağ, Otsu Metot ve bu metotların birleşiminden oluşan hibrid metotlar kullanılarak beyin tümör segmentasyon sistemleri geliştirilmiştir. Bu metotların segmentasyon başarı oranları tespit edilmiş ve birbirleriyle karşılaştırılmıştır. Geliştirilen sistemlerde, ilk olarak MRI beyin görüntülerini girdi olarak alınır, sonra kafatası bölgesinin görüntüden ayrılması, önişleme ve Bulanık C-Ortalamalar, K-Ortalamalar, Özdüzenlemeli Ağ, Otsu metot gibi algoritmalarla segmentasyon işlemleri uygulanır. Önişlemden önce, kafatası bölgesi, MRI beyin görüntüsü veri setindeki görüntülerden çıkarılır. Ön işlemede, beyin görüntülerinin kalitesi iyileştirilir ve görüntülerin gürültüsü, çeşitli filtreleme ve morfolojik tekniklerle kaldırılır. Son olarak, kümeleme ve eşikleme teknikleri ile beynin tümör bölgesi tespit edildi. Daha sonra, segmentasyon sistemleri değerlendirildi ve doğruluk, gerçek pozitif oranı ve gerçek negatif oranına göre birbirleriyle karşılaştırıldı.Anahtar Kelimeler: Beyin Tümörü Segmentasyonu, Tıbbi Görüntüleme, Bulanık C-Ortalamalar, K-Ortalamalar, Özdüzenlemeli Ağ, Otsu Meto

A Modality-Adaptive Method for Segmenting Brain Tumors and Organs-at-Risk in Radiation Therapy Planning

In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas. The method combines a contrast-adaptive generative model for whole-brain segmentation with a new spatial regularization model of tumor shape using convolutional restricted Boltzmann machines. We demonstrate experimentally that the method is able to adapt to image acquisitions that differ substantially from any available training data, ensuring its applicability across treatment sites; that its tumor segmentation accuracy is comparable to that of the current state of the art; and that it captures most organs-at-risk sufficiently well for radiation therapy planning purposes. The proposed method may be a valuable step towards automating the delineation of brain tumors and organs-at-risk in glioblastoma patients undergoing radiation therapy.Comment: corrected one referenc

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection