A Novel Fully Automated MRI-Based Deep Learning Method for Classification of IDH Mutation Status in Brain Gliomas

ABSTRACTBackgroundIsocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. Currently, reliable IDH mutation determination requires invasive surgical procedures. The purpose of this study was to develop a highly-accurate, MRI-based, voxel-wise deep-learning IDH-classification network using T2-weighted (T2w) MR images and compare its performance to a multi-contrast network.MethodsMulti-parametric brain MRI data and corresponding genomic information were obtained for 214 subjects (94 IDH-mutated, 120 IDH wild-type) from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). Two separate networks were developed including a T2w image only network (T2-net) and a multi-contrast (T2w, FLAIR, and T1 post-contrast), network (TS-net) to perform IDH classification and simultaneous single label tumor segmentation. The networks were trained using 3D-Dense-UNets. A three-fold cross-validation was performed to generalize the networks’ performance. ROC analysis was also performed. Dice-scores were computed to determine tumor segmentation accuracy.ResultsT2-net demonstrated a mean cross-validation accuracy of 97.14% +/-0.04 in predicting IDH mutation status, with a sensitivity of 0.97 +/-0.03, specificity of 0.98 +/-0.01, and an AUC of 0.98 +/-0.01. TS-net achieved a mean cross-validation accuracy of 97.12% +/-0.09, with a sensitivity of 0.98 +/-0.02, specificity of 0.97 +/-0.001, and an AUC of 0.99 +/-0.01. The mean whole tumor segmentation Dice-scores were 0.85 +/-0.009 for T2-net and 0.89 +/-0.006 for TS-net.ConclusionWe demonstrate high IDH classification accuracy using only T2-weighted MRI. This represents an important milestone towards clinical translation.Keypoints – 1IDH status is an important prognostic marker for gliomas. 2. We developed a non-invasive, MRI based, highly accurate deep-learning method for the determination of IDH status 3. The deep-learning networks utilizes only T2 weighted MR images to predict IDH status thereby facilitating clinical translation.IMPORTANCE OF THE STUDYOne of the most important recent discoveries in brain glioma biology has been the identification of the isocitrate dehydrogenase (IDH) mutation status as a marker for therapy and prognosis. The mutated form of the gene confers a better prognosis and treatment response than gliomas with the non-mutated or wild-type form. Currently, the only reliable way to determine IDH mutation status is to obtain glioma tissue either via an invasive brain biopsy or following open surgical resection. The ability to non-invasively determine IDH mutation status has significant implications in determining therapy and predicting prognosis. We developed a highly accurate, deep learning network that utilizes only T2-weighted MR images and outperforms previously published methods. The high IDH classification accuracy of our T2w image only network (T2-net) marks an important milestone towards clinical translation. Imminent clinical translation is feasible because T2-weighted MR imaging is widely available and routinely performed in the assessment of gliomas.

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A Novel Fully Automated Mri-Based Deep Learning Method for Classification of Idh Mutation Status in Brain Gliomas

Neuro-Oncology ◽

10.1093/neuonc/noz199 ◽

2019 ◽

Cited By ~ 7

Author(s):

Chandan Ganesh Bangalore Yogananda ◽

Bhavya R Shah ◽

Maryam Vejdani-Jahromi ◽

Sahil S Nalawade ◽

Gowtham K Murugesan ◽

...

Keyword(s):

Deep Learning ◽

Cross Validation ◽

Brain Mri ◽

The Cancer Genome Atlas ◽

Tumor Segmentation ◽

Idh Mutation ◽

Mr Images ◽

Post Contrast ◽

Mutation Status ◽

Brain Gliomas

Abstract Background Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. Currently, reliable IDH mutation determination requires invasive surgical procedures. The purpose of this study was to develop a highly-accurate, MRI-based, voxel-wise deep-learning IDH-classification network using T2-weighted (T2w) MR images and compare its performance to a multi-contrast network. Methods Multi-parametric brain MRI data and corresponding genomic information were obtained for 214 subjects (94 IDH-mutated, 120 IDH wild-type) from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). Two separate networks were developed including a T2w image only network (T2-net) and a multi-contrast (T2w, FLAIR, and T1 post-contrast) network (TS-net) to perform IDH classification and simultaneous single label tumor segmentation. The networks were trained using 3D-Dense-UNets. Three-fold cross-validation was performed to generalize the networks’ performance. ROC analysis was also performed. Dice-scores were computed to determine tumor segmentation accuracy. Results T2-net demonstrated a mean cross-validation accuracy of 97.14% ±0.04 in predicting IDH mutation status, with a sensitivity of 0.97 ±0.03, specificity of 0.98 ±0.01, and an AUC of 0.98 ±0.01. TS-net achieved a mean cross-validation accuracy of 97.12% ±0.09, with a sensitivity of 0.98 ±0.02, specificity of 0.97 ±0.001, and an AUC of 0.99 ±0.01. The mean whole tumor segmentation Dice-scores were 0.85 ±0.009 for T2-net and 0.89 ±0.006 for TS-net. Conclusion We demonstrate high IDH classification accuracy using only T2-weighted MR images. This represents an important milestone towards clinical translation.

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A Novel Fully Automated MRI-Based Deep Learning Method for Classification of 1P/19Q Co-Deletion Status in Brain Gliomas

10.1101/2020.07.15.204933 ◽

2020 ◽

Author(s):

Chandan Ganesh Bangalore Yogananda ◽

Bhavya R. Shah ◽

Frank F. Yu ◽

Marco C. Pinho ◽

Sahil S. Nalawade ◽

...

Keyword(s):

Deep Learning ◽

Cross Validation ◽

Tumor Segmentation ◽

Idh Mutation ◽

Mr Images ◽

Mutation Status ◽

Learning Network ◽

Genomic Marker ◽

Deep Learning Network ◽

Brain Gliomas

ABSTRACTBackgroundOne of the most important recent discoveries in brain glioma biology has been the identification of the isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion status as markers for therapy and prognosis. 1p/19q co-deletion is the defining genomic marker for oligodendrogliomas and confers a better prognosis and treatment response than gliomas without it. Our group has previously developed a highly accurate deep-learning network for determining IDH mutation status using T2-weighted MRI only. The purpose of this study was to develop a similar 1p/19q deep-learning classification network.MethodsMulti-parametric brain MRI and corresponding genomic information were obtained for 368 subjects from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). 1p/19 co-deletions were present in 130 subjects. 238 subjects were non co-deleted. A T2w image only network (1p/19q-net) was developed to perform 1p/19q co-deletion status classification and simultaneous single-label tumor segmentation using 3D-Dense-UNets. Threefold cross-validation was performed to generalize the network performance. ROC analysis was also performed. Dice-scores were computed to determine tumor segmentation accuracy.Results1p/19q-net demonstrated a mean cross validation accuracy of 93.46% across the 3 folds (93.4%, 94.35%, and 92.62%, standard dev=0.8) in predicting 1p/19q co-deletion status with a sensitivity and specificity of 0.90 ±0.003 and 0.95 ±0.01, respectively and a mean AUC of 0.95 ±0.01. The whole tumor segmentation mean Dice-score was 0.80 ± 0.007.ConclusionWe demonstrate high 1p/19q co-deletion classification accuracy using only T2-weighted MR images. This represents an important milestone toward using MRI to predict glioma histology, prognosis, and response to treatment.Keypoints1. 1p/19 co-deletion status is an important genetic marker for gliomas. 2. We developed a non-invasive, MRI based, highly accurate deep-learning method for the determination of 1p/19q co-deletion status that only utilizes T2 weighted MR imagesIMPORTANCE OF THE STUDYOne of the most important recent discoveries in brain glioma biology has been the identification of the isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion status as markers for therapy and prognosis. 1p/19q co-deletion is the defining genomic marker for oligodendrogliomas and confers a better prognosis and treatment response than gliomas without it. Currently, the only reliable way to determine 1p/19q mutation status requires analysis of glioma tissue obtained either via an invasive brain biopsy or following open surgical resection. The ability to non-invasively determine 1p/19q co-deletion status has significant implications in determining therapy and predicting prognosis. We developed a highly accurate, deep learning network that utilizes only T2-weighted MR images and outperforms previously published imagebased methods. The high classification accuracy of our T2w image only network (1p/19q-net) in predicting 1p/19q co-deletion status marks an important step towards image-based stratification of brain gliomas. Imminent clinical translation is feasible because T2-weighted MR imaging is widely available and routinely performed in the assessment of gliomas.

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Classification of Brain Tumor IDH Status using MRI and Deep Learning

10.1101/757344 ◽

2019 ◽

Author(s):

Sahil Nalawade ◽

Gowtham Murugesan ◽

Maryam Vejdani-Jahromi ◽

Ryan A. Fisicaro ◽

Chandan Ganesh Bangalore Yogananda ◽

...

Keyword(s):

Deep Learning ◽

Classification Accuracy ◽

Test Accuracy ◽

Low Grade ◽

Idh Mutation ◽

Validation Data ◽

Mutation Status ◽

Radiologic Imaging ◽

Address Data

AbstractIsocitrate dehydrogenase (IDH) mutation status is an important marker in glioma diagnosis and therapy. We propose a novel automated pipeline for predicting IDH status noninvasively using deep learning and T2-weighted (T2w) MR images with minimal preprocessing (N4 bias correction and normalization to zero mean and unit variance). T2w MRI and genomic data were obtained from The Cancer Imaging Archive dataset (TCIA) for 260 subjects (120 High grade and 140 Low grade gliomas). A fully automated 2D densely connected model was trained to classify IDH mutation status on 208 subjects and tested on another held-out set of 52 subjects, using 5-fold cross validation. Data leakage was avoided by ensuring subject separation during the slice-wise randomization. Mean classification accuracy of 90.5% was achieved for each axial slice in predicting the three classes of no tumor, IDH mutated and IDH wild-type. Test accuracy of 83.8% was achieved in predicting IDH mutation status for individual subjects on the test dataset of 52 subjects. We demonstrate a deep learning method to predict IDH mutation status using T2w MRI alone. Radiologic imaging studies using deep learning methods must address data leakage (subject duplication) in the randomization process to avoid upward bias in the reported classification accuracy.

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Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics

Neuro-Oncology ◽

10.1093/neuonc/noaa177 ◽

2020 ◽

Cited By ~ 1

Author(s):

Yoon Seong Choi ◽

Sohi Bae ◽

Jong Hee Chang ◽

Seok-Gu Kang ◽

Se Hoon Kim ◽

...

Keyword(s):

Hybrid Model ◽

Hybrid Approach ◽

University Hospital ◽

Tumor Segmentation ◽

Idh Mutation ◽

Mr Images ◽

Mutation Status ◽

Severance Hospital ◽

National University ◽

Fluid Attenuated Inversion Recovery

Abstract Background Glioma prognosis depends on isocitrate dehydrogenase (IDH) mutation status. We aimed to predict the IDH status of gliomas from preoperative MR images using a fully automated hybrid approach with convolutional neural networks (CNNs) and radiomics. Methods We reviewed 1166 preoperative MR images of gliomas (grades II–IV) from Severance Hospital (n = 856), Seoul National University Hospital (SNUH; n = 107), and The Cancer Imaging Archive (TCIA; n = 203). The Severance set was subdivided into the development (n = 727) and internal test (n = 129) sets. Based on T1 postcontrast, T2, and fluid-attenuated inversion recovery images, a fully automated model was developed that comprised a CNN for tumor segmentation (Model 1) and CNN-based classifier for IDH status prediction (Model 2) that uses a hybrid approach based on 2D tumor images and radiomic features from 3D tumor shape and loci guided by Model 1. The trained model was tested on internal (a subset of the Severance set) and external (SNUH and TCIA) test sets. Results The CNN for tumor segmentation (Model 1) achieved a dice coefficient of 0.86–0.92 across datasets. Our hybrid model achieved accuracies of 93.8%, 87.9%, and 78.8%, with areas under the receiver operating characteristic curves of 0.96, 0.94, and 0.86 and areas under the precision-recall curves of 0.88, 0.82, and 0.81 in the internal test, SNUH, and TCIA sets, respectively. Conclusions Our fully automated hybrid model demonstrated the potential to be a highly reproducible and generalizable tool across different datasets for the noninvasive prediction of the IDH status of gliomas.

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A novel fully automated MRI-based deep-learning method for classification of 1p/19q co-deletion status in brain gliomas

Neuro-Oncology Advances ◽

10.1093/noajnl/vdaa066 ◽

2020 ◽

Vol 2 (1) ◽

Author(s):

Chandan Ganesh Bangalore Yogananda ◽

Bhavya R Shah ◽

Frank F Yu ◽

Marco C Pinho ◽

Sahil S Nalawade ◽

...

Keyword(s):

Deep Learning ◽

Cross Validation ◽

Brain Mri ◽

Area Under The Curve ◽

The Cancer Genome Atlas ◽

Response To Treatment ◽

Tumor Segmentation ◽

Idh Mutation ◽

Characteristic Analysis ◽

Genomic Marker

Abstract Background One of the most important recent discoveries in brain glioma biology has been the identification of the isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion status as markers for therapy and prognosis. 1p/19q co-deletion is the defining genomic marker for oligodendrogliomas and confers a better prognosis and treatment response than gliomas without it. Our group has previously developed a highly accurate deep-learning network for determining IDH mutation status using T2-weighted (T2w) MRI only. The purpose of this study was to develop a similar 1p/19q deep-learning classification network. Methods Multiparametric brain MRI and corresponding genomic information were obtained for 368 subjects from The Cancer Imaging Archive and The Cancer Genome Atlas. 1p/19 co-deletions were present in 130 subjects. Two-hundred and thirty-eight subjects were non-co-deleted. A T2w image-only network (1p/19q-net) was developed to perform 1p/19q co-deletion status classification and simultaneous single-label tumor segmentation using 3D-Dense-UNets. Three-fold cross-validation was performed to generalize the network performance. Receiver operating characteristic analysis was also performed. Dice scores were computed to determine tumor segmentation accuracy. Results 1p/19q-net demonstrated a mean cross-validation accuracy of 93.46% across the 3 folds (93.4%, 94.35%, and 92.62%, SD = 0.8) in predicting 1p/19q co-deletion status with a sensitivity and specificity of 0.90 ± 0.003 and 0.95 ± 0.01, respectively and a mean area under the curve of 0.95 ± 0.01. The whole tumor segmentation mean Dice score was 0.80 ± 0.007. Conclusion We demonstrate high 1p/19q co-deletion classification accuracy using only T2w MR images. This represents an important milestone toward using MRI to predict glioma histology, prognosis, and response to treatment.

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IDH mutation status is associated with distinct vascular gene expression signatures in lower-grade gliomas

Neuro-Oncology ◽

10.1093/neuonc/noy088 ◽

2018 ◽

Vol 20 (11) ◽

pp. 1505-1516 ◽

Cited By ~ 15

Author(s):

Lei Zhang ◽

Liqun He ◽

Roberta Lugano ◽

Kenney Roodakker ◽

Michael Bergqvist ◽

...

Keyword(s):

Gene Expression ◽

Endothelial Cells ◽

Lower Grade ◽

Idh Mutation ◽

Wild Type ◽

Who Grade ◽

Mutation Status ◽

Grade Ii ◽

Grade Ii Glioma

Abstract Background Vascular gene expression patterns in lower-grade gliomas (LGGs; diffuse World Health Organization [WHO] grades II–III gliomas) have not been thoroughly investigated. The aim of this study was to molecularly characterize LGG vessels and determine if tumor isocitrate dehydrogenase (IDH) mutation status affects vascular phenotype. Methods Gene expression was analyzed using an in-house dataset derived from microdissected vessels and total tumor samples from human glioma in combination with expression data from 289 LGG samples available in the database of The Cancer Genome Atlas. Vascular protein expression was examined by immunohistochemistry in human brain tumor tissue microarrays (TMAs) representing WHO grades II–IV gliomas and nonmalignant brain samples. Regulation of gene expression was examined in primary endothelial cells in vitro. Results Gene expression analysis of WHO grade II glioma indicated an intermediate stage of vascular abnormality, less severe than that of glioblastoma vessels but distinct from normal vessels. Enhanced expression of laminin subunit alpha 4 (LAMA4) and angiopoietin 2 (ANGPT2) in WHO grade II glioma was confirmed by staining of human TMAs. IDH wild-type LGGs displayed a specific angiogenic gene expression signature, including upregulation of ANGPT2 and serpin family H (SERPINH1), connected to enhanced endothelial cell migration and matrix remodeling. Transcription factor analysis indicated increased transforming growth factor beta (TGFβ) and hypoxia signaling in IDH wild-type LGGs. A subset of genes specifically induced in IDH wild-type LGG vessels was upregulated by stimulation of endothelial cells with TGFβ2, vascular endothelial growth factor, or cobalt chloride in vitro. Conclusion IDH wild-type LGG vessels are molecularly distinct from the vasculature of IDH-mutated LGGs. TGFβ and hypoxia-related signaling pathways may be potential targets for anti-angiogenic therapy of IDH wild-type LGG.

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Magnetic Resonance Imaging Derived Biomarkers of IDH Mutation Status and Overall Survival in Grade III Astrocytomas

Diagnostics ◽

10.3390/diagnostics10040247 ◽

2020 ◽

Vol 10 (4) ◽

pp. 247

Author(s):

Paola Feraco ◽

Antonella Bacci ◽

Patrizia Ferrazza ◽

Luc van den Hauwe ◽

Riccardo Pertile ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Overall Survival ◽

Magnetic Resonance ◽

Idh Mutation ◽

Wild Type ◽

Resonance Imaging ◽

Characteristic Analysis ◽

Mutation Status ◽

Mutational Status ◽

Adc Values

The evaluation of the isocitrate dehydrogenase (IDH) mutation status in the glioma decision-making process has diagnostic, prognostic and therapeutic implications. The aim of this study was to evaluate whether conventional magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) can noninvasively predict the most common IDH mutational status (R132H) in GIII-astrocytomas and the overall survival (OS). Hence, twenty-two patients (9-F, 13-M) with a histological diagnosis of GIII-astrocytoma and evaluation of IDH-mutation status (12-wild type, 10-mutant) were retrospectively evaluated. Imaging studies were reviewed for the morphological feature and mean ADC values (ADCm). Statistics included a Fisher’s exact test, Student’s t-test, Spearman’s Test and receiver operating characteristic analysis. A p ≤ 0.05 value was considered statistically significant for all the tests. A younger age and a frontal location were more likely related to mutational status. IDH-wild type (Wt) exhibited a slight enhancement (p = 0.039). The ADCm values in IDH-mutant (Mut) patients were higher than those of IDH-Wt patients (p < 0.0004). The value of ADC ≥ 0.99 × 10−3 mm2/s emerged as a “cut-off” to differentiate the mutation state. In the overall group, a positive relationship between the ADCm values and OS was detected (p = 0.003; r = 0.62). Adding quantitative measures of ADC values to conventional MR imaging could be used routinely as a noninvasive marker of specific molecular patterns.

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Pharmacoresistant seizures and IDH mutation in low grade gliomas

Neuro-Oncology Advances ◽

10.1093/noajnl/vdab146 ◽

2021 ◽

Author(s):

Carlos Eduardo Correia ◽

Yoshie Umemura ◽

Jessica R Flynn ◽

Anne S Reiner ◽

Edward K Avila

Keyword(s):

Tumor Resection ◽

Incidence Rates ◽

P Value ◽

Low Grade ◽

Idh Mutation ◽

Wild Type ◽

Mutation Status ◽

Low Grade Gliomas ◽

Idh1 R132h ◽

Idh Mutations

Abstract Purpose Many low-grade gliomas (LGG) harbor isocitrate dehydrogenase (IDH) mutations. Although IDH mutation is known to be epileptogenic, the rate of refractory seizures in LGG with IDH mutation vs wild-type had not been previously compared. We therefore compared seizure pharmacoresistance in IDH-mutated and wild-type LGGs. Methods Single-institution retrospective study of patients with histologic proven LGG, known IDH mutation status, seizures, and ≥ 2 neurology clinic encounters. Seizure history was followed until histological high-grade transformation or death. Seizures requiring ≥ 2 changes in anti-epileptic drugs were considered pharmacoresistant. Incidence rates of pharmacoresistant seizures were estimated using competing risks methodology. Results Of 135 patients, 25 patients (19%) had LGGs classified as IDH wild-type. Of those with IDH mutation, 104 (94.5%) were IDH1 R132H; only six were IDH2 R172K. 120 patients (89%) had tumor resection and 14 (10%) had biopsy. Initial post-surgical management included observation (64%), concurrent chemoradiation (23%), chemotherapy alone (9%), and radiotherapy alone (4%). Seizures became pharmacoresistant in 24 IDH-mutated patients (22%) and in 3 IDH wild-type patients (12%). The 4-year cumulative incidence of intractable seizures was 17.6% (95% CI: 10.6%-25.9%) in IDH-mutated and 11% (95% CI: 1.3%-32.6%) in IDH wild-type LGG (Gray’s P-value= 0.26). Conclusions 22% of the IDH-mutated patients developed pharmacoresistant seizures, compared to 12% of the IDH wild-type tumors.The likelihood of developing pharmacoresistant seizures in patients with LGG-related epilepsy is independent to IDH mutation status, however, IDH-mutated tumors were approximately twice as likely to experience LGG-related pharmacoresistant seizures.

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Recognization and Systematization of MR Images using K Means Clustering and DNN

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.f3811.049620 ◽

2020 ◽

Vol 9 (6) ◽

pp. 924-927

Keyword(s):

Deep Learning ◽

Brain Tumor ◽

Brain Tumors ◽

Digital Health ◽

High Accuracy ◽

Tumor Segmentation ◽

Mr Images ◽

Brain Tumor Segmentation ◽

Brain Tumor Diagnosis ◽

The Brain

Brain tumors are the result of unusual growth and unrestrained cell disunity in the brain. Most of the medical image application lack in segmentation and labeling. Brain tumors can lead to loss of lives if they are not detected early and correctly. Recently, deep learning has been an important role in the field of digital health. One of its action is the reduction of manual decision in the diagnosis of diseases specifically brain tumor diagnosis needs high accuracy, where minute errors in judgment may lead to loss therefore, brain tumor segmentation is an necessary challenge in medical side. In recent time numerous ,methods exist for tumor segmentation with lack of accuracy. Deep learning is used to achieve the goal of brain tumor segmentation. In this work, three network of brain MR images segmentation is employed .A single network is compared to achieve segmentation of MR images using separate network .In this paper segmentation has improved and result is obtained with high accuracy and efficiency.

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MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning

BMC Medical Imaging ◽

10.1186/s12880-020-00543-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Dominik Müller ◽

Frank Kramer

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Medical Image ◽

Cross Validation ◽

Data Augmentation ◽

Semantic Segmentation ◽

Medical Image Segmentation ◽

Tumor Segmentation ◽

Data Set ◽

Public Data

Abstract Background The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. Still, current image segmentation platforms do not provide the required functionalities for plain setup of medical image segmentation pipelines. Already implemented pipelines are commonly standalone software, optimized on a specific public data set. Therefore, this paper introduces the open-source Python library MIScnn. Implementation The aim of MIScnn is to provide an intuitive API allowing fast building of medical image segmentation pipelines including data I/O, preprocessing, data augmentation, patch-wise analysis, metrics, a library with state-of-the-art deep learning models and model utilization like training, prediction, as well as fully automatic evaluation (e.g. cross-validation). Similarly, high configurability and multiple open interfaces allow full pipeline customization. Results Running a cross-validation with MIScnn on the Kidney Tumor Segmentation Challenge 2019 data set (multi-class semantic segmentation with 300 CT scans) resulted into a powerful predictor based on the standard 3D U-Net model. Conclusions With this experiment, we could show that the MIScnn framework enables researchers to rapidly set up a complete medical image segmentation pipeline by using just a few lines of code. The source code for MIScnn is available in the Git repository: https://github.com/frankkramer-lab/MIScnn.

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