Automatic segmentation, internal classification, and follow-up of optic pathway gliomas in MRI

Segmentation of Low-Grade Brain Tumors Using Mutual Attention Multimodal MRI

Early detection and precise characterization of brain tumors play a crucial role in improving patient outcomes and extending survival rates. Among neuroimaging modalities, magnetic resonance imaging (MRI) is the gold standard for brain tumor diagnostics due to its ability to produce high-contrast images across a variety of sequences, each highlighting distinct tissue characteristics. This study focuses on enabling multimodal MRI sequences to advance the automatic segmentation of low-grade astrocytomas, a challenging task due to their diffuse and irregular growth patterns. A novel mutual-attention deep learning framework is proposed, which integrates complementary information from multiple MRI sequences, including T2-weighted and fluid-attenuated inversion recovery (FLAIR) sequences, to enhance the segmentation accuracy. Unlike conventional segmentation models, which treat each modality independently or simply concatenate them, our model introduces mutual attention mechanisms. This allows the network to dynamically focus on salient features across modalities by jointly learning interdependencies between imaging sequences, leading to more precise boundary delineations even in regions with subtle tumor signals. The proposed method is validated using the UCSF-PDGM dataset, which consists of 35 astrocytoma cases, presenting a realistic and clinically challenging dataset. The results demonstrate that T2w/FLAIR modalities contribute most significantly to the segmentation performance. The mutual-attention model achieves an average Dice coefficient of 0.87. This study provides an innovative pathway toward improving segmentation of low-grade tumors by enabling context-aware fusion across imaging sequences. Furthermore, the study showcases the clinical relevance of integrating AI with multimodal MRI, potentially improving non-invasive tumor characterization and guiding future research in radiological diagnostics.

Surgical options of chiasmatic hypothalamic glioma-a relevant part of therapy in an interdisciplinary approach for tumor control

OBJECTIVE

The extent of resection of pediatric low-grade glioma mostly improves progression-free survival. In chiasmatic hypothalamic glioma (CHG), complete resections are limited due to the relevantly high risk of associated neurological and endocrinological deficits. Still, surgery might have its role in the framework of a multidisciplinary team (MDT) approach. We report our retrospective experience from two centers on surgical options and their impact on long-term outcomes.

METHODS

Medical records of surgically treated pediatric CHG patients between 2004 and 2022 were analyzed. Patient characteristics, surgical interventions, histology, and non-surgical therapy were retrieved together with outcome measures such as visual acuity, endocrine function, and survival.

RESULTS

A total of 63 patients (33 female, NF-1, n = 8) were included. Age at first diagnosis was 4.6 years (range 0.2-16.9) and cohort follow-up was 108 ± 72 months. Twenty patients were surgically treated with a biopsy and 43 patients with debulking at a median age of 6.5 years (range 0.16-16.9). Patients received a median of 2 tumor surgeries (range 1-5). Cyst drainage was accomplished in 15 patients, and 27 patients had ventriculoperitoneal shunt implantation. Non-surgical therapy was given in 69.8%. At the end of follow-up, 74.6% of patients had stable disease. The cohort had a median Karnofsky score of 90 (range 0-100). Four patients died. Hormone substitution was necessary in 30.2%, and visual acuity was impaired in 66% of patients.

CONCLUSION

Pediatric CHG is a chronic disease due to overall high survival with multiple progressions. Surgical therapy remains a key treatment option offering biopsy, limited tumor-debulking, cyst fenestration, and hydrocephalus management in the framework of MDT decision-making. Team experience contributes to reducing possible deficits in this challenging cohort.

Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review

OBJECTIVES

Medical image analysis practices face challenges that can potentially be addressed with algorithm-based segmentation tools. In this study, we map the field of automatic MR brain lesion segmentation to understand the clinical applicability of prevalent methods and study designs, as well as challenges and limitations in the field.

DESIGN

Scoping review.

SETTING

Three databases (PubMed, IEEE Xplore and Scopus) were searched with tailored queries. Studies were included based on predefined criteria. Emerging themes during consecutive title, abstract, methods and whole-text screening were identified. The full-text analysis focused on materials, preprocessing, performance evaluation and comparison.

RESULTS

Out of 2990 unique articles identified through the search, 441 articles met the eligibility criteria, with an estimated growth rate of 10% per year. We present a general overview and trends in the field with regard to publication sources, segmentation principles used and types of lesions. Algorithms are predominantly evaluated by measuring the agreement of segmentation results with a trusted reference. Few articles describe measures of clinical validity.

CONCLUSIONS

The observed reporting practices leave room for improvement with a view to studying replication, method comparison and clinical applicability. To promote this improvement, we propose a list of recommendations for future studies in the field.

Visual function tests including the role of optical coherence tomography in neurofibromatosis 1

Optic pathway glioma (OPG) is a common and significant complication of neurofibromatosis 1 (NF-1) that might lead to vision loss. The main reason to treat OPG is to preserve vision. Tumor location along the visual pathway largely dictates the presenting signs and symptoms. Clinical ophthalmic evaluation is focused on optic nerve functions including evaluation of pupils' reaction to light, visual acuity, color vision, and visual field, as well as optic nerve appearance. An important relatively new ancillary test is optic coherence tomography (OCT) that measures the volume of retinal nerve fiber layer around the optic nerve and the ganglion cell layer-inner plexiform layer (GCL-IPL) of the macula, both proved to be strongly associated with losing vision in OPG. Accurate evaluation of vision functions plays a critical role in the decision of treatment. In this review, we describe the ophthalmological assessment including new biomarkers in clinical use. We also outline prognostic factors and current recommendations for surveillance and indications for treatment.

Neurofibromatosis 1-associated optic pathway gliomas

BACKGROUND

Optic Pathway Gliomas (OPG) are the most common brain tumor in Neurofibromatosis 1 patients (NF1). They are found along the optic pathway and may involve the optic nerves, chiasm, retro-chiasmatic structures, and the optic radiations. NF1 associate OPG (NF1-OPG) have variable presentation, disease course and response to treatment. The optimal management is patient-specific and should be tailored by a multidisciplinary team. Age, sex, histology, and molecular markers may be important factors in the individualized decision-making process. Chemotherapy is the first-line treatment in cases of progressive tumors, and visual preservation is the main goal of treatment.

PURPOSE

In this paper we will review the disease, practical management, and recent advances of NF1-OPG.

Automatic segmentation, classification, and follow-up of optic pathway gliomas using deep learning and fuzzy c-means clustering based on MRI

PURPOSE

Optic pathway gliomas (OPG) are low-grade pilocytic astrocytomas accounting for 3-5% of pediatric intracranial tumors. Accurate and quantitative follow-up of OPG using magnetic resonance imaging (MRI) is crucial for therapeutic decision making, yet is challenging due to the complex shape and heterogeneous tissue pattern which characterizes these tumors. The aim of this study was to implement automatic methods for segmentation and classification of OPG and its components, based on MRI.

METHODS

A total of 202 MRI scans from 29 patients with chiasmatic OPG scanned longitudinally were retrospectively collected and included in this study. Data included T₂ and post-contrast T₁ weighted images. The entire tumor volume and its components were manually annotated by a senior neuro-radiologist, and inter- and intra-rater variability of the entire tumor volume was assessed in a subset of scans. Automatic tumor segmentation was performed using deep-learning method with U-Net+ResNet architecture. A fivefold cross-validation scheme was used to evaluate the automatic results relative to manual segmentation. Voxel-based classification of the tumor into enhanced, non-enhanced, and cystic components was performed using fuzzy c-means clustering.

RESULTS

The results of the automatic tumor segmentation were: mean dice score = 0.736 ± 0.025, precision = 0.918 ± 0.014, and recall = 0.635 ± 0.039 for the validation data, and dice score = 0.761 ± 0.011, precision = 0.794 ± 0.028, and recall = 0.742 ± 0.012 for the test data. The accuracy of the voxel-based classification of tumor components was 0.94, with precision = 0.89, 0.97, and 0.85, and recall = 1.00, 0.79, and 0.94 for the non-enhanced, enhanced, and cystic components, respectively.

CONCLUSION

This study presents methods for automatic segmentation of chiasmatic OPG tumors and classification into the different components of the tumor, based on conventional MRI. Automatic quantitative longitudinal assessment of these tumors may improve radiological monitoring, facilitate early detection of disease progression and optimize therapy management.

Impact of repeated operations for progressive low-grade gliomas

BACKGROUND

Maximal, aggressive resection of diffuse low-grade gliomas (DLGG) is well established as the standard of care in neuro-oncology. The role of repeat resection for tumor progression is unclear.

OBJECTIVE

To assess the role of repeated operation for DLGG, and the effect on malignant transformation and survival.

METHODS

We conducted a historical cohort study in which all patients undergoing multiple resections of DLGG between the years 1995-2019 were evaluated for overall survival (OS) and time to transformation (TTT). We then compared the outcome of this group with that of a matched control group comprised of patients who underwent only one operation despite being eligible for repeat surgery at tumor progression, but had received non-surgical oncological therapy or declined additional treatment.

RESULTS

Of 607 patients in our departmental DLGG database, 93 patients underwent 2 or more surgeries and had sufficient follow-up and imaging data to be included in the study group. Thirty-eight patients were included in the matched control group. Early (less than 1 year) progression was associated with decreased survival and shorter TTT in the study group. Patients undergoing multiple resections had significantly longer TTT and OS compared to patients who underwent a single surgery. This effect was especially noted in patients who had radiological evidence of tumor transformation.

CONCLUSIONS

Repeated resections of LGG are safe and offer survival benefit in select patients. Early progression following resection is associated with worse prognosis. Patients with evidence of radiological transformation may benefit the most from re-resection.

Surveillance magnetic resonance imaging for isolated optic pathway gliomas: is gadolinium necessary?

BACKGROUND

Pediatric optic pathway gliomas are typically indolent but have a variable clinical course. Treatment is dictated by symptoms and changes on contrast-enhanced MRI examinations. Gadolinium retention in children has motivated parsimonious use of gadolinium-based contrast agents.

OBJECTIVES

To determine surveillance MR factors that motivate changes in tumor-directed therapies and extrapolate cost-efficacy of a non-contrast follow-up protocol.

MATERIALS AND METHODS

Using an imaging database search we identified children with isolated optic pathway gliomas and ≥3 follow-up contrast-enhanced MRIs. We reviewed medical records and imaging for: (1) coincident changes on contrast-enhanced MRI and tumor-directed therapy, (2) demographics and duration of follow-up, (3) motivations for intervention, (4) assessment of gadolinium-based contrast agents' utility and (5) health care utilization data. We assessed cost impact in terms of relative value unit (RVU) burden.

RESULTS

We included 17 neurofibromatosis type 1 (NF1) and 21 non-NF1 patients who underwent a median 16.9 and 24.3 cumulative contrast-enhanced MR exams over 7.7 years and 8.1 years of follow-up, respectively. Eight children (one with NF1) had intervention based on contrast-enhanced MR findings alone. For these eight, increased tumor size was the only common feature, and it was apparent on non-contrast T2 sequences. For the median patient, a non-contrast follow-up protocol could result in 15.9 (NF1) and 23.3 (non-NF1) fewer gadolinium-based contrast agent administrations, and a 39% lower yearly RVU burden.

CONCLUSION

Pediatric patients with isolated optic pathway gliomas undergo a large number of routine contrast-enhanced MR follow-up exams. Gadolinium might not be needed for these exams to inform management decisions. Secondary benefits of a non-contrast follow-up protocol include decreased cost and risk to the patient.

Isolated optic nerve gliomas: a multicenter historical cohort study

OBJECTIVE Isolated optic nerve gliomas (IONGs) constitute a rare subgroup of optic pathway gliomas (OPGs). Due to the rarity of this condition and the difficulty in differentiating IONGs from other types of OPGs in most clinical series, little is known about these tumors. Currently, due to lack of evidence, they are managed the same as any other OPG. METHODS The authors conducted a multicenter retrospective cohort study aimed at determining the natural history of IONGs. Included were patients with clear-cut glioma of the optic nerve without posterior (chiasmatic/hypothalamic) involvement. At least 1 year of follow-up, 2 MRI studies, and 2 neuro-ophthalmological examinations were required for inclusion. RESULTS Thirty-six patients with 39 tumors were included in this study. Age at diagnosis ranged between 6 months and 16 years (average 6 years). The mean follow-up time was 5.6 years. Twenty-five patients had neurofibromatosis Type 1. During the follow-up period, 59% of the tumors progressed, 23% remained stable, and 18% (all with neurofibromatosis Type 1) displayed some degree of spontaneous regression. Fifty-one percent of the patients presented with visual decline, of whom 90% experienced further deterioration. Nine patients were treated with chemotherapy, 5 of whom improved visually. Ten patients underwent operation, and no local or distal recurrence was noted. CONCLUSIONS Isolated optic nerve gliomas are highly dynamic tumors. Radiological progression and visual deterioration occur in greater percentages than in the general population of patients with OPGs. Response to chemotherapy may be better in this group, and its use should be considered early in the course of the disease.

Computer-based radiological longitudinal evaluation of meningiomas following stereotactic radiosurgery

PURPOSE

Stereotactic radiosurgery (SRS) is a common treatment for intracranial meningiomas. SRS is planned on a pre-therapy gadolinium-enhanced T1-weighted MRI scan (Gd-T1w MRI) in which the meningioma contours have been delineated. Post-SRS therapy serial Gd-T1w MRI scans are then acquired for longitudinal treatment evaluation. Accurate tumor volume change quantification is required for treatment efficacy evaluation and for treatment continuation.

METHOD

We present a new algorithm for the automatic segmentation and volumetric assessment of meningioma in post-therapy Gd-T1w MRI scans. The inputs are the pre- and post-therapy Gd-T1w MRI scans and the meningioma delineation in the pre-therapy scan. The output is the meningioma delineations and volumes in the post-therapy scan. The algorithm uses the pre-therapy scan and its meningioma delineation to initialize an extended Chan-Vese active contour method and as a strong patient-specific intensity and shape prior for the post-therapy scan meningioma segmentation. The algorithm is automatic, obviates the need for independent tumor localization and segmentation initialization, and incorporates the same tumor delineation criteria in both the pre- and post-therapy scans.

RESULTS

Our experimental results on retrospective pre- and post-therapy scans with a total of 32 meningiomas with volume ranges 0.4-26.5 cm[Formula: see text] yield a Dice coefficient of [Formula: see text]% with respect to ground-truth delineations in post-therapy scans created by two clinicians. These results indicate a high correspondence to the ground-truth delineations.

CONCLUSION

Our algorithm yields more reliable and accurate tumor volume change measurements than other stand-alone segmentation methods. It may be a useful tool for quantitative meningioma prognosis evaluation after SRS.

Manifold Embedding and Semantic Segmentation for Intraoperative Guidance With Hyperspectral Brain Imaging

Recent advances in hyperspectral imaging have made it a promising solution for intra-operative tissue characterization, with the advantages of being non-contact, non-ionizing, and non-invasive. Working with hyperspectral images in vivo, however, is not straightforward as the high dimensionality of the data makes real-time processing challenging. In this paper, a novel dimensionality reduction scheme and a new processing pipeline are introduced to obtain a detailed tumor classification map for intra-operative margin definition during brain surgery. However, existing approaches to dimensionality reduction based on manifold embedding can be time consuming and may not guarantee a consistent result, thus hindering final tissue classification. The proposed framework aims to overcome these problems through a process divided into two steps: dimensionality reduction based on an extension of the T-distributed stochastic neighbor approach is first performed and then a semantic segmentation technique is applied to the embedded results by using a Semantic Texton Forest for tissue classification. Detailed in vivo validation of the proposed method has been performed to demonstrate the potential clinical value of the system.

Growth hormone excess in children with neurofibromatosis type-1 and optic glioma

In children with neurofibromatosis type 1 (NF1) and optic pathways glioma (OPG), growth hormone (GH) excess has been rarely reported and mainly associated to central precocious puberty. The aim of our study is to evaluate the prevalence of GH excess, the association with central precocious puberty, the relation with tumor site and the evolution over time in a large cohort of children with NF1 and OPG. Sixty-four NF1 children with OPG were evaluated. Patients with stature and/or height velocity >2 SD for age were studied for GH secretion. Seven out of 64 children (10.9%) with NF1 and optic pathways glioma showed GH excess, isolated in 5 cases and associated to central precocious puberty in 2. All the children with GH excess had a tumor involving the chiasma. Children with GH excess underwent medical treatment with lanreotide and a minimum clinical/biochemical follow up of 2 years is reported. The present study demonstrates that GH excess should be considered as a relative frequent endocrine manifestation in NF1 patients, similarly to central precocious puberty. Therefore, these patients should undergo frequent accurate auxologic evaluations. On the other hand, an increase in height velocity in children with NF1, even despite normal ophthalmological exams, can suggest the presence of OPG and therefore represents an indication to perform brain MRI.

Brain tumor classification and segmentation using sparse coding and dictionary learning

This paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods.

Optic pathway glioma volume predicts retinal axon degeneration in neurofibromatosis type 1

OBJECTIVE

To determine whether tumor size is associated with retinal nerve fiber layer (RNFL) thickness, a measure of axonal degeneration and an established biomarker of visual impairment in children with optic pathway gliomas (OPGs) secondary to neurofibromatosis type 1 (NF1).

METHODS

Children with NF1-OPGs involving the optic nerve (extension into the chiasm and tracts permitted) who underwent both volumetric MRI analysis and optical coherence tomography (OCT) within 2 weeks of each other were included. Volumetric measurement of the entire anterior visual pathway (AVP; optic nerve, chiasm, and tract) was performed using high-resolution T1-weighted MRI. OCT measured the average RNFL thickness around the optic nerve. Linear regression models evaluated the relationship between RNFL thickness and AVP dimensions and volume.

RESULTS

Thirty-eight participants contributed 55 study eyes. The mean age was 5.78 years. Twenty-two participants (58%) were female. RNFL thickness had a significant negative relationship to total AVP volume and total brain volume (p < 0.05, all comparisons). For every 1 mL increase in AVP volume, RNFL thickness declined by approximately 5 microns. A greater AVP volume of OPGs involving the optic nerve and chiasm, but not the tracts, was independently associated with a lower RNFL thickness (p < 0.05). All participants with an optic chiasm volume >1.3 mL demonstrated axonal damage (i.e., RNFL thickness <80 microns).

CONCLUSIONS

Greater OPG and AVP volume predicts axonal degeneration, a biomarker of vision loss, in children with NF1-OPGs. MRI volumetric measures may help stratify the risk of visual loss from NF1-OPGs.

Compressed sensing for longitudinal MRI: An adaptive-weighted approach

PURPOSE

Repeated brain MRI scans are performed in many clinical scenarios, such as follow up of patients with tumors and therapy response assessment. In this paper, the authors show an approach to utilize former scans of the patient for the acceleration of repeated MRI scans.

METHODS

The proposed approach utilizes the possible similarity of the repeated scans in longitudinal MRI studies. Since similarity is not guaranteed, sampling and reconstruction are adjusted during acquisition to match the actual similarity between the scans. The baseline MR scan is utilized both in the sampling stage, via adaptive sampling, and in the reconstruction stage, with weighted reconstruction. In adaptive sampling, k-space sampling locations are optimized during acquisition. Weighted reconstruction uses the locations of the nonzero coefficients in the sparse domains as a prior in the recovery process. The approach was tested on 2D and 3D MRI scans of patients with brain tumors.

RESULTS

The longitudinal adaptive compressed sensing MRI (LACS-MRI) scheme provides reconstruction quality which outperforms other CS-based approaches for rapid MRI. Examples are shown on patients with brain tumors and demonstrate improved spatial resolution. Compared with data sampled at the Nyquist rate, LACS-MRI exhibits signal-to-error ratio (SER) of 24.8 dB with undersampling factor of 16.6 in 3D MRI.

CONCLUSIONS

The authors presented an adaptive method for image reconstruction utilizing similarity of scans in longitudinal MRI studies, where possible. The proposed approach can significantly reduce scanning time in many applications that consist of disease follow-up and monitoring of longitudinal changes in brain MRI.

Prechiasmatic transection of the optic nerve in optic nerve glioma: technical description and surgical outcome

Optic pathway glioma (OPG) encompasses a spectrum of findings ranging from lesions confined to the optic nerve only, lesions affecting the optic chiasm and hypothalamus, and lesions with diffuse involvement of a large part of the optic pathway and neighboring structures. The majority of pediatric low-grade astrocytomas in the optic/chiasmatic region are typical pilocytic astrocytoma. The rest of them (10 %) may be other gliomas such as fibrillary pilomyxoid astrocytoma (grade 2 WHO). The postsurgical local recurrence rate of 55 to 76 % has been reported in some histological subtypes such as pilomyxoid astrocytoma (grade 2). Performing a prechiasmatic transection might offer a new surgical option to avoid further tumor growth toward the chiasm in the optic nerve glioma with predominantly orbital manifestations. In this retrospective study, four patients (three children, two without neurofibromatosis type 1 (NF1), and one with NF1 and one adult without NF1) with optic nerve glioma without involvement of the chiasm but blindness, disfiguring proptosis, and pain of the affected eye were included. The surgical approach was performed as a combined approach from pterional extradural and intradural. Without any exceptions, vision of the contralateral eye could be preserved and did not show any deterioration after surgery or during the follow-up time between 17 and 106 months. Furthermore, in all patients, gross total tumor resection could be achieved. During follow-up observation in all patients, no further tumor progress or recurrences could be observed. None of the patients were treated postoperatively by radiotherapy or chemotherapy. Prechiasmatic transection of the optic nerve in optic nerve glioma without affecting the chiasm might offer a surgical treatment option to control tumor growth and to preserve vision of the contralateral eye.

Automatic Region-Based Brain Classification of MRI-T1 Data

Image segmentation of medical images is a challenging problem with several still not totally solved issues, such as noise interference and image artifacts. Region-based and histogram-based segmentation methods have been widely used in image segmentation. Problems arise when we use these methods, such as the selection of a suitable threshold value for the histogram-based method and the over-segmentation followed by the time-consuming merge processing in the region-based algorithm. To provide an efficient approach that not only produce better results, but also maintain low computational complexity, a new region dividing based technique is developed for image segmentation, which combines the advantages of both regions-based and histogram-based methods. The proposed method is applied to the challenging applications: Gray matter (GM), White matter (WM) and cerebro-spinal fluid (CSF) segmentation in brain MR Images. The method is evaluated on both simulated and real data, and compared with other segmentation techniques. The obtained results have demonstrated its improved performance and robustness.

Pre-Adult MRI of Brain Cancer and Neurological Injury: Multivariate Analyses

Brain cancer and neurological injuries, such as stroke, are life-threatening conditions for which further research is needed to overcome the many challenges associated with providing optimal patient care. Multivariate analysis (MVA) is a class of pattern recognition technique involving the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of neuroimaging challenges, including identifying variables associated with patient outcomes; understanding an injury's etiology, development, and progression; creating diagnostic tests; assisting in treatment monitoring; and more. Compared to adults, imaging of the developing brain has attracted less attention from MVA researchers, however, remarkable MVA growth has occurred in recent years. This paper presents the results of a systematic review of the literature focusing on MVA technologies applied to brain injury and cancer in neurological fetal, neonatal, and pediatric magnetic resonance imaging (MRI). With a wide variety of MRI modalities providing physiologically meaningful biomarkers and new biomarker measurements constantly under development, MVA techniques hold enormous potential toward combining available measurements toward improving basic research and the creation of technologies that contribute to improving patient care.

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.

The effect of chemotherapy on optic pathway gliomas and their sub-components: A volumetric MR analysis study

BACKGROUND

Optic pathway gliomas (OPG) represent 5% of pediatric brain tumors and compose a major therapeutic dilemma to the treating physicians. While chemotherapy is widely used for these tumors, our ability to predict radiological response is still lacking. In this study, we use volumetric imaging to examine in detail the long-term effect of chemotherapy on the tumor as well as its various sub-components.

PROCEDURE

The tumors of 15 patients with OPG, treated with chemotherapy, were longitudinally measured using our novel, previously described volumetric method. Patients were treated with up to five lines of chemotherapy. Sufficient follow-up imaging data, and patient's numbers, allowed for analysis of two treatment lines. Volumetric measurements of the tumors were segmented into solid-non-enhancing, solid-enhancing, and cystic components. Outcome analysis was done per specific treatment line and for the overall follow-up period.

RESULTS

An average reduction of 9.7% (±23%) in the gross-total-solid volume (GTSV) was noted following treatment with vincristine and carboplatin. The cystic component grew under therapy by an average of 12.6% (±39%). When measured over the course of the whole study period, the cystic component grew by an average of 35% (±100%) and the GTSV increased by 12% (±35%).

CONCLUSION

Initial treatment with vincristine and carboplatin seems to have a minimal initial effect, mostly on the solid components. The cystic component in itself seems to be unaffected by chemotherapy, and contributes to the subsequent growth of the total volume. During the overall treatment period, both solid and cystic components grew regardless of combined treatment methods.

Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images

Resection of brain tumors is a tricky task in surgery due to its direct influence on the patients' survival rate. Determining the tumor resection extent for its complete information via-à-vis volume and dimensions in pre- and post-operative Magnetic Resonance Images (MRI) requires accurate estimation and comparison. The active contour segmentation technique is used to segment brain tumors on pre-operative MR images using self-developed software. Tumor volume is acquired from its contours via alpha shape theory. The graphical user interface is developed for rendering, visualizing and estimating the volume of a brain tumor. Internet Brain Segmentation Repository dataset (IBSR) is employed to analyze and determine the repeatability and reproducibility of tumor volume. Accuracy of the method is validated by comparing the estimated volume using the proposed method with that of gold-standard. Segmentation by active contour technique is found to be capable of detecting the brain tumor boundaries. Furthermore, the volume description and visualization enable an interactive examination of tumor tissue and its surrounding. Admirable features of our results demonstrate that alpha shape theory in comparison to other existing standard methods is superior for precise volumetric measurement of tumor.

Disambiguating the optic nerve from the surrounding cerebrospinal fluid: Application to MS-related atrophy

PURPOSE

Our goal is to develop an accurate, automated tool to characterize the optic nerve (ON) and cerebrospinal fluid (CSF) to better understand ON changes in disease.

METHODS

Multi-atlas segmentation is used to localize the ON and sheath on T2-weighted MRI (0.6 mm(3) resolution). A sum of Gaussian distributions is fit to coronal slice-wise intensities to extract six descriptive parameters, and a regression forest is used to map the model space to radii. The model is validated for consistency using tenfold cross-validation and for accuracy using a high resolution (0.4 mm(2) reconstructed to 0.15 mm(2)) in vivo sequence. We evaluated this model on 6 controls and 6 patients with multiple sclerosis (MS) and a history of optic neuritis.

RESULTS

In simulation, the model was found to have an explanatory R-squared for both ON and sheath radii greater than 0.95. The accuracy of the method was within the measurement error on the highest possible in vivo resolution. Comparing healthy controls and patients with MS, significant structural differences were found near the ON head and the chiasm, and structural trends agreed with the literature.

CONCLUSION

This is a first demonstration that the ON can be exclusively, quantitatively measured and separated from the surrounding CSF using MRI.

Optic pathway gliomas

Optic pathway gliomas (OPGs) are among the most challenging neoplasms in modern pediatric neuro-oncology. Recent technological advances in imaging, surgery, and chemotherapy may lead to better understanding of the pathophysiology and better clinical results. This chapter reviews these advances and the current treatment paradigms.

Optic pathway gliomas in adults

BACKGROUND

Optic pathway gliomas (OPGs) are considered relatively benign pediatric tumors. Adult patients with OPG can be divided into 2 groups: adult patients with tumors diagnosed in childhood and adult patients diagnosed during adulthood.

OBJECTIVE

To characterize the clinical course of adult patients with OPG.

METHODS

We retrospectively collected clinical and imaging data of all adult OPG patients monitored in our medical center between 1990 and 2012.

RESULTS

Twenty-two adult patients were included. Age at diagnosis varied widely (6 months-66 years), as did age at last follow-up (18-74 years). Ten patients were diagnosed at adulthood and 12 in childhood. Of the patients diagnosed at childhood, 6 had radiological progression during childhood, and 3 of those patients suffered visual impairment. From this group, 1 patient had further progression during adulthood accompanied by additional visual decline, and 2 patients had additional visual decline during adulthood despite no signs of progression. Of the 6 patients whose tumors were stable during childhood, all 6 remained stable during adulthood. Of 10 patients diagnosed at adulthood, 6 patients suffered visual deterioration; in 5 of them, a concomitant progression was noted. Two patients were diagnosed with high-grade gliomas.

CONCLUSION

OPGs may be active during childhood or adulthood. Those patients who experienced anatomic activity during childhood are prone to continue experiencing active disease during adulthood. A significant percentage of patients diagnosed with low-grade OPG at adulthood may suffer progression, visual decline, or both.

ABBREVIATIONS

NF1, neurofibromatosis 1OPG, optic pathway gliomas.

Semiautomatic segmentation and follow-up of multicomponent low-grade tumors in longitudinal brain MRI studies

PURPOSE

Tracking the progression of low grade tumors (LGTs) is a challenging task, due to their slow growth rate and associated complex internal tumor components, such as heterogeneous enhancement, hemorrhage, and cysts. In this paper, the authors show a semiautomatic method to reliably track the volume of LGTs and the evolution of their internal components in longitudinal MRI scans.

METHODS

The authors' method utilizes a spatiotemporal evolution modeling of the tumor and its internal components. Tumor components gray level parameters are estimated from the follow-up scan itself, obviating temporal normalization of gray levels. The tumor delineation procedure effectively incorporates internal classification of the baseline scan in the time-series as prior data to segment and classify a series of follow-up scans. The authors applied their method to 40 MRI scans of ten patients, acquired at two different institutions. Two types of LGTs were included: Optic pathway gliomas and thalamic astrocytomas. For each scan, a "gold standard" was obtained manually by experienced radiologists. The method is evaluated versus the gold standard with three measures: gross total volume error, total surface distance, and reliability of tracking tumor components evolution.

RESULTS

Compared to the gold standard the authors' method exhibits a mean Dice similarity volumetric measure of 86.58% and a mean surface distance error of 0.25 mm. In terms of its reliability in tracking the evolution of the internal components, the method exhibits strong positive correlation with the gold standard.

CONCLUSIONS

The authors' method provides accurate and repeatable delineation of the tumor and its internal components, which is essential for therapy assessment of LGTs. Reliable tracking of internal tumor components over time is novel and potentially will be useful to streamline and improve follow-up of brain tumors, with indolent growth and behavior.

Semi-automatic segmentation of brain tumors using population and individual information

Efficient segmentation of tumors in medical images is of great practical importance in early diagnosis and radiation plan. This paper proposes a novel semi-automatic segmentation method based on population and individual statistical information to segment brain tumors in magnetic resonance (MR) images. First, high-dimensional image features are extracted. Neighborhood components analysis is proposed to learn two optimal distance metrics, which contain population and patient-specific information, respectively. The probability of each pixel belonging to the foreground (tumor) and the background is estimated by the k-nearest neighborhood classifier under the learned optimal distance metrics. A cost function for segmentation is constructed through these probabilities and is optimized using graph cuts. Finally, some morphological operations are performed to improve the achieved segmentation results. Our dataset consists of 137 brain MR images, including 68 for training and 69 for testing. The proposed method overcomes segmentation difficulties caused by the uneven gray level distribution of the tumors and even can get satisfactory results if the tumors have fuzzy edges. Experimental results demonstrate that the proposed method is robust to brain tumor segmentation.

Quantitative tumor segmentation for evaluation of extent of glioblastoma resection to facilitate multisite clinical trials

Standard-of-care therapy for glioblastomas, the most common and aggressive primary adult brain neoplasm, is maximal safe resection, followed by radiation and chemotherapy. Because maximizing resection may be beneficial for these patients, improving tumor extent of resection (EOR) with methods such as intraoperative 5-aminolevulinic acid fluorescence-guided surgery (FGS) is currently under evaluation. However, it is difficult to reproducibly judge EOR in these studies due to the lack of reliable tumor segmentation methods, especially for postoperative magnetic resonance imaging (MRI) scans. Therefore, a reliable, easily distributable segmentation method is needed to permit valid comparison, especially across multiple sites. We report a segmentation method that combines versatile region-of-interest blob generation with automated clustering methods. We applied this to glioblastoma cases undergoing FGS and matched controls to illustrate the method's reliability and accuracy. Agreement and interrater variability between segmentations were assessed using the concordance correlation coefficient, and spatial accuracy was determined using the Dice similarity index and mean Euclidean distance. Fuzzy C-means clustering with three classes was the best performing method, generating volumes with high agreement with manual contouring and high interrater agreement preoperatively and postoperatively. The proposed segmentation method allows tumor volume measurements of contrast-enhanced T 1-weighted images in the unbiased, reproducible fashion necessary for quantifying EOR in multicenter trials.

A survey of MRI-based medical image analysis for brain tumor studies

MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.

GLISTR: glioma image segmentation and registration

We present a generative approach for simultaneously registering a probabilistic atlas of a healthy population to brain magnetic resonance (MR) scans showing glioma and segmenting the scans into tumor as well as healthy tissue labels. The proposed method is based on the expectation maximization (EM) algorithm that incorporates a glioma growth model for atlas seeding, a process which modifies the original atlas into one with tumor and edema adapted to best match a given set of patient's images. The modified atlas is registered into the patient space and utilized for estimating the posterior probabilities of various tissue labels. EM iteratively refines the estimates of the posterior probabilities of tissue labels, the deformation field and the tumor growth model parameters. Hence, in addition to segmentation, the proposed method results in atlas registration and a low-dimensional description of the patient scans through estimation of tumor model parameters. We validate the method by automatically segmenting 10 MR scans and comparing the results to those produced by clinical experts and two state-of-the-art methods. The resulting segmentations of tumor and edema outperform the results of the reference methods, and achieve a similar accuracy from a second human rater. We additionally apply the method to 122 patients scans and report the estimated tumor model parameters and their relations with segmentation and registration results. Based on the results from this patient population, we construct a statistical atlas of the glioma by inverting the estimated deformation fields to warp the tumor segmentations of patients scans into a common space.