Multi-channel MRI segmentation of eye structures and tumors using patient-specific features

Magnetic Resonance Imaging in the Clinical Care for Uveal Melanoma Patients-A Systematic Review from an Ophthalmic Perspective

Conversely to most tumour types, magnetic resonance imaging (MRI) was rarely used for eye tumours. As recent technical advances have increased ocular MRI's diagnostic value, various clinical applications have been proposed. This systematic review provides an overview of the current status of MRI in the clinical care of uveal melanoma (UM) patients, the most common eye tumour in adults. In total, 158 articles were included. Two- and three-dimensional anatomical scans and functional scans, which assess the tumour micro-biology, can be obtained in routine clinical setting. The radiological characteristics of the most common intra-ocular masses have been described extensively, enabling MRI to contribute to diagnoses. Additionally, MRI's ability to non-invasively probe the tissue's biological properties enables early detection of therapy response and potentially differentiates between high- and low-risk UM. MRI-based tumour dimensions are generally in agreement with conventional ultrasound (median absolute difference 0.5 mm), but MRI is considered more accurate in a subgroup of anteriorly located tumours. Although multiple studies propose that MRI's 3D tumour visualisation can improve therapy planning, an evaluation of its clinical benefit is lacking. In conclusion, MRI is a complementary imaging modality for UM of which the clinical benefit has been shown by multiple studies.

An Automatic Framework to Create Patient-specific Eye Models From 3D Magnetic Resonance Images for Treatment Selection in Patients With Uveal Melanoma

PURPOSE

The optimal treatment strategy for uveal melanoma (UM) relies on many factors, the most important being tumor size and location. Building on recent developments in high-resolution 3D ocular magnetic resonance imaging (MRI), we developed an automatic image-processing framework to create patient-specific eye models and to subsequently determine the full 3D tumor shape and size automatically.

METHODS AND MATERIALS

From 15 patients with UM, 3D inversion-recovery gradient-echo (T1-weighted) and 3D fat-suppressed spin-echo (T2-weighted) images were acquired with a 7T MRI scanner. First, the sclera and cornea were segmented from the T2-weighted image by mesh-fitting. The T1- and T2-weighted images were then coregistered. From the registered T1-weighted image, the lens, vitreous body, retinal detachment, and tumor were segmented. Fuzzy C-means clustering was used to differentiate the tumor from retinal detachments. The tumor model was verified and (if needed) edited by an ophthalmic MRI specialist. Subsequently, the prominence and largest basal diameter of the tumor were measured automatically based on the verified contours. These results were compared with manual assessments on the original images and with ultrasound measurements to show the errors in manual analysis.

RESULTS

The framework successfully created an eye model fully automatically in 12 cases. In these cases, a Dice similarity coefficient (mean surface distance) of 97.7%±0.84% (0.17±0.11 mm) was achieved for the sclera, 96.8%±1.05% (0.20±0.06 mm) for the vitreous body, 91.6%±4.83% (0.15±0.06 mm) for the lens, and 86.0%±7.4% (0.35±0.27 mm) for the tumor. The manual assessments deviated, on average, 0.39±0.31 mm in prominence and 1.7±1.22 mm in basal diameter from the automatic measurements.

CONCLUSIONS

The described framework combined information from T1- and T2-weighted images to accurately determine tumor boundaries in 3D. The proposed process may have a direct effect on clinical workflow, as it enables an accurate 3D assessment of tumor dimensions, which directly influences therapy selection.

Multi-view convolutional neural networks for automated ocular structure and tumor segmentation in retinoblastoma

In retinoblastoma, accurate segmentation of ocular structure and tumor tissue is important when working towards personalized treatment. This retrospective study serves to evaluate the performance of multi-view convolutional neural networks (MV-CNNs) for automated eye and tumor segmentation on MRI in retinoblastoma patients. Forty retinoblastoma and 20 healthy-eyes from 30 patients were included in a train/test (N = 29 retinoblastoma-, 17 healthy-eyes) and independent validation (N = 11 retinoblastoma-, 3 healthy-eyes) set. Imaging was done using 3.0 T Fast Imaging Employing Steady-state Acquisition (FIESTA), T2-weighted and contrast-enhanced T1-weighted sequences. Sclera, vitreous humour, lens, retinal detachment and tumor were manually delineated on FIESTA images to serve as a reference standard. Volumetric and spatial performance were assessed by calculating intra-class correlation (ICC) and dice similarity coefficient (DSC). Additionally, the effects of multi-scale, sequences and data augmentation were explored. Optimal performance was obtained by using a three-level pyramid MV-CNN with FIESTA, T2 and T1c sequences and data augmentation. Eye and tumor volumetric ICC were 0.997 and 0.996, respectively. Median [Interquartile range] DSC for eye, sclera, vitreous, lens, retinal detachment and tumor were 0.965 [0.950-0.975], 0.847 [0.782-0.893], 0.975 [0.930-0.986], 0.909 [0.847-0.951], 0.828 [0.458-0.962] and 0.914 [0.852-0.958], respectively. MV-CNN can be used to obtain accurate ocular structure and tumor segmentations in retinoblastoma.

Three-dimensional MRI-based treatment planning approach for non-invasive ocular proton therapy

PURPOSE

To develop a high-resolution three-dimensional (3D) magnetic resonance imaging (MRI)-based treatment planning approach for uveal melanomas (UM) in proton therapy.

MATERIALS/METHODS

For eight patients with UM, a segmentation of the gross tumor volume (GTV) and organs-at-risk (OARs) was performed on T1- and T2-weighted 7 Tesla MRI image data to reconstruct the patient MR-eye. An extended contour was defined with a 2.5-mm isotropic margin derived from the GTV. A broad beam algorithm, which we have called πDose, was implemented to calculate relative proton absorbed doses to the ipsilateral OARs. Clinically favorable gazing angles of the treated eye were assessed by calculating a global weighted-sum objective function, which set penalties for OARs and extreme gazing angles. An optimizer, which we have named OPT'im-Eye-Tool, was developed to tune the parameters of the functions for sparing critical-OARs.

RESULTS

In total, 441 gazing angles were simulated for every patient. Target coverage including margins was achieved in all the cases (V95%  > 95%). Over the whole gazing angles solutions space, maximum dose (Dmax ) to the optic nerve and the macula, and mean doses (Dmean ) to the lens, the ciliary body and the sclera were calculated. A forward optimization was applied by OPT'im-Eye-Tool in three different prioritizations: iso-weighted, optic nerve prioritized, and macula prioritized. In each, the function values were depicted in a selection tool to select the optimal gazing angle(s). For example, patient 4 had a T2 equatorial tumor. The optimization applied for the straight gazing angle resulted in objective function values of 0.46 (iso-weighted situation), 0.90 (optic nerve prioritization) and 0.08 (macula prioritization) demonstrating the impact of that angle in different clinical approaches.

CONCLUSIONS

The feasibility and suitability of a 3D MRI-based treatment planning approach have been successfully tested on a cohort of eight patients diagnosed with UM. Moreover, a gaze-angle trade-off dose optimization with respect to OARs sparing has been developed. Further validation of the whole treatment process is the next step in the goal to achieve both a non-invasive and a personalized proton therapy treatment.

Ophthalmic Magnetic Resonance Imaging: Where Are We (Heading To)?

Magnetic resonance imaging of the eye and orbit (MReye) is a cross-domain research field, combining (bio)physics, (bio)engineering, physiology, data sciences and ophthalmology. A growing number of reports document technical innovations of MReye and promote their application in preclinical research and clinical science. Realizing the progress and promises, this review outlines current trends in MReye. Examples of MReye strategies and their clinical relevance are demonstrated. Frontier applications in ocular oncology, refractive surgery, ocular muscle disorders and orbital inflammation are presented and their implications for explorations into ophthalmic diseases are provided. Substantial progress in anatomically detailed, high-spatial resolution MReye of the eye, orbit and optic nerve is demonstrated. Recent developments in MReye of ocular tumors are explored, and its value for personalized eye models derived from machine learning in the treatment planning of uveal melanoma and evaluation of retinoblastoma is highlighted. The potential of MReye for monitoring drug distribution and for improving treatment management and the assessment of individual responses is discussed. To open a window into the eye and into (patho)physiological processes that in the past have been largely inaccessible, advances in MReye at ultrahigh magnetic field strengths are discussed. A concluding section ventures a glance beyond the horizon and explores future directions of MReye across multiple scales, including in vivo electrolyte mapping of sodium and other nuclei. This review underscores the need for the (bio)medical imaging and ophthalmic communities to expand efforts to find solutions to the remaining unsolved problems and technical obstacles of MReye, with the objective to transfer methodological advancements driven by MR physics into genuine clinical value.

Diagnostic performance of MRI of post-laminar optic nerve invasion detection in retinoblastoma: A systematic review and meta-analysis

PURPOSE

Preoperative MRI detection of post-laminar optic nerve invasion (PLONI) offers guidance in assessing the probability of total tumor resection, an estimation of the extent of surgery, and screening of candidates for eye-preserving therapies or neoadjuvant chemotherapies in the patients with retinoblastoma (RB). The purpose of this systematic review and meta-analysis was to evaluate the diagnostic performance of MRI for detecting PLONI in patients with RB and to demonstrate the factors that may influence the diagnostic performance.

METHODS

Ovid-MEDLINE and EMBASE databases were searched up to January 11, 2020, for studies identifying the diagnostic performance of MRI for detecting PLONI in patients with RB. The pooled sensitivity and specificity of all studies were calculated followed by meta-regression analysis.

RESULTS

Twelve (1240 patients, 1255 enucleated globes) studies were included. The pooled sensitivity was 61%, and the pooled specificity was 88%. Higgins I² statistic demonstrated moderate heterogeneity in the sensitivity (I² = 72.23%) and specificity (I² = 78.11%). Spearman correlation coefficient indicated the presence of a threshold effect. In the meta-regression, higher magnetic field strength (3 T than 1.5 T), performing fat suppression, and thinner slice thickness (< 3 mm) were factors causing heterogeneity and enhancing diagnostic power across the included studies.

CONCLUSIONS

MR imaging was demonstrated to have acceptable diagnostic performance in detecting PLONI in patients with RB. The variation in the magnetic field strength and protocols was the main factor behind the heterogeneity across the included studies. Therefore, there is room for developing and optimizing the MR protocols for patients with RB.

A deep learning approach to automatic detection of early glaucoma from visual fields

PURPOSE

To investigate the suitability of multi-scale spatial information in 30o visual fields (VF), computed from a Convolutional Neural Network (CNN) classifier, for early-glaucoma vs. control discrimination.

METHOD

Two data sets of VFs acquired with the OCTOPUS 101 G1 program and the Humphrey Field Analyzer 24-2 pattern were subdivided into control and early-glaucomatous groups, and converted into a new image using a novel voronoi representation to train a custom-designed CNN so to discriminate between control and early-glaucomatous eyes. Saliency maps that highlight what regions of the VF are contributing maximally to the classification decision were computed to provide classification justification. Model fitting was cross-validated and average precision (AP) score performances were computed for our method, Mean Defect (MD), square-root of Loss Variance (sLV), their combination (MD+sLV), and a Neural Network (NN) that does not use convolutional features.

RESULTS

CNN achieved the best AP score (0.874±0.095) across all test folds for one data set compared to others (MD = 0.869±0.064, sLV = 0.775±0.137, MD+sLV = 0.839±0.085, NN = 0.843±0.089) and the third best AP score (0.986 ±0.019) on the other one with slight difference from the other methods (MD = 0.986±0.023, sLV = 0.992±0.016, MD+sLV = 0.987±0.017, NN = 0.985±0.017). In general, CNN consistently led to high AP across different data sets. Qualitatively, computed saliency maps appeared to provide clinically relevant information on the CNN decision for individual VFs.

CONCLUSION

The proposed CNN offers high classification performance for the discrimination of control and early-glaucoma VFs when compared with standard clinical decision measures. The CNN classification, aided by saliency visualization, may support clinicians in the automatic discrimination of early-glaucomatous and normal VFs.