# An Automated 2D U-Net Segmentation Method for the Identification of Cancer Brain Metastases Using MRI Images

Vangelis Tzardis, Efthyvoulos Kyriacou, Christos P. Loizou, Anastasia Constantinidou

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Vangelis Tzardis, Efthyvoulos Kyriacou, Christos P. Loizou, Anastasia Constantinidou. An Automated 2D U-Net Segmentation Method for the Identification of Cancer Brain Metastases Using MRI Images. 18th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2022, Hersonissos, Greece. pp.161-173, 10.1007/978-3-031-08341-9_14. hal-05335627

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# An Automated 2D U-Net Segmentation Method
for the Identification of Cancer Brain
Metastases Using MRI Images

Vangelis Tzardis<fnref n="1" />(✉) ID, Efthyvoulos Kyriacou<fnref n="1" /> ID, Christos P. Loizou<fnref n="1" /> ID,
and Anastasia Constantinidou<fnref n="2" />,<fnref n="3" /> ID

**Abstract.** In this study, we propose an automated system for the segmentation of cancer brain metastases (CBM) using MRI images. The goal is the correlation with regards to the primary cancer site. The segmentation of CBM is a challenging task due to their wide range in terms of number, shape, size and location in the brain. We experimented with the training of a modified U-Net convolutional neural network (CNN) using $N = 3474$ brain image slices for training, $N_v = 579$ for validation and $N_T = 579$ for testing from the public dataset BrainMetShare. The proposed model was evaluated on the testing data ($N_T$), on a lesion-cross section basis with areas from 2.8 to 1225.7 mm<fnref n="2" /> and yielded a mean Sensitivity (SE) 0.70 ± 0.30, Specificity (SP) 0.77 ± 0.26 and Dice similarity coefficient (DSC) of 0.73 ± 0.29 across the entire dataset. The present results show the good agreement of the proposed method with the ground truth.

**Keywords:** Magnetic resonance imaging · Brain metastasis · Cancer brain metastasis · Automated image segmentation · Convolutional neural network · U-Net

## 1 Introduction

Cancer brain metastasis (CBM) is the most common intracranial malignant cancer in the adult population, by far outnumbering the primary brain tumors. According to [1], it is estimated that approximately 20% of the cancer patients will develop CBM. Breast cancer, lung cancer and melanoma are the most frequent sites to develop CBM. Breast cancer brain metastases (BCBM) accounts for 5–20% of all metastatic cancers, whereas CBM originating in lung and melanoma cancer appears in 20–56% and 7–16% of all cases respectively [1]. Additionally, CBM patients suffer from a poor quality of life,

<footnote n="1">¹ Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus
University of Technology, 3036 Limassol, Cyprus
em.tzardis@edu.cut.ac.cy, {efthyvoulos.kyriacou, christos.loizou}@cut.ac.cy</footnote>

<footnote n="2">² University of Cyprus, 2029 Nicosia, Cyprus
constantinidou.anastasia@ucy.ac.cy</footnote>

<footnote n="3">³ Bank of Cyprus Oncology Centre, 2006 Nicosia, Cyprus</footnote>
a debilitating symptomatology and their future survival prognosis is often at one year [1]. These criteria necessitate the early detection of CBMs in order to select the best treatment option. Recent studies suggested that, along with systemic therapy, surgery and radiation, CBM patients' treatment should also be based on their overall performance status as well as the primary tumor site, its molecular subtype, the number, location and the size of the CBM [2].

Currently, the diagnosis of CBM is performed based on clinical information, magnetic resonance imaging (MRI) modalities and histopathological examination (biopsy). Even though biopsy is considered today as the gold standard for cancer diagnosis [1], it poses several challenges especially when discussing CBM and the risks during the biopsy procedure. MRI assessment of CBM in terms of localization and tissue characteristics, on the other hand, is a time-consuming and tedious procedure requiring the assistance of a medical professional, and typically including significant inter- and intra-reader variability. To cope with these issues, the research community has come up with computer-aided decision support systems to automate the segmentation and classification of CBM according to their primary cancer site, as described below.

For the segmentation task on CBM, a variety of Deep Learning (DL) methods has been identified as presented in [3–11]. More specifically, Liu et al. [4] and Charron et al. [5], both modified the known 3D DeepMedic convolutional neural network (CNN) [6], with the former adding an extra pathway for the local features, and the latter experimenting with the network hyper-parameters and data augmentation with virtual patients. Bousabarah et al. [7], demonstrated an ensemble of 2D U-Nets [8], with each one trained on a different subset of data or with a different loss function. In two other studies, Xue et al. [9] and Zhue et al. [10], cascaded two fully CNN, respectively 3D (full height) and 3-slice tall. Grøvik et al. [11], modified the DeepLab V3 method [12], in which they concatenated three MRI modalities as an input tensor, with five 2D slices per modality. In addition, the team made use of dilated convolutions and stochastic dropout of modalities. All above studies used a subset of the pre- and post-contrast T₁-weighted as well as fluid-attenuated inversion recovery MRI modalities. Their results showed insufficiency of the models for CBM, which were smaller than 6 mm in diameter. More specifically, in [10], a Dice similarity coefficient (DSC) = 0.64 ± 0.2 for CBM between 3 and 6 mm was reported. Notably, the best DSC was reported at 0.84 ± 0.07 for CBM larger than 5 mm [9] and 0.81 ± 0.15 (averaged per CBM volume), for CBM in the range of 1–52 mm [10] respectively.

For the BCBM classification task, current methods proposed in the literature [13–15], using quantitative imaging features are not mature enough to be clinically applicable. More specifically Kniep et al. [13], showed that imaging features such as texture, shape, first-order statistics and wavelet decompositions are only complementary to the larger predictive power of age and sex data in BCBM. Ortiz-Ramón et al. [14], documented that only fair results (Area Under the Curve (AUC) = 60%), could be achieved, for breast cancer in multi-class prediction mainly due to misclassifications with melanoma. Finally, in [15] Béresová et al. reported statistically significant differentiations between texture features both on images and local binary pattern maps (LBP) from CBM originating in breast and lung, whereas without developing a decision-making classifier.
Due to the aforementioned challenges, segmentation and identification of CBM, particularly in small ones, is still challenging and a work in progress. Through this study, we demonstrate the first steps towards the development of an integrated and fully automated system for CBM segmentation and BCBM classification.

# 2 Methodology

## 2.1 Proposed System Overview

In this study, we experimented with the segmentation of CBM, with results presented in Sect. 3. The proposed method for this task was based on our previous work [16], in which a modified 2D U-net was used (see also Fig. 1d). Also, the selection of this segmentation network for this study was based on the high accuracy results of U-Nets in brain tumors (gliomas). It was also based on the fact that it can achieve a DSC = 0.90 [17] as well as on the proposition that generalizable state of the art (as tested on gliomas) can be accomplished by optimizing the training hyper-parameters [18], without the need to develop elaborate architectures.

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Overview of the proposed integrated system for the automated brain metastasis segmentation and classification of the primary site of cancer. The light gray dashed boxes relates to the experimental implementation of the segmentation task as proposed in this work. (Color figure online)</figcaption></figure>

The remaining system modules described in the following, are the main steps of our future work aiming at the BCBM classification. Based on Fig. 1, we propose to register and localize the segmented CBM lesions to existing brain atlases (see also Fig. 1g), such as to the Montreal Neurological Institute (MNI) 152 atlases database [13]. Then, the initial segmented images will be preprocessed (see Fig. 1e), and fed to various imaging feature extractors (see Fig. 1f), as the ones described in the previous paragraph. In
addition, we also intend to use a new class of features based on amplitude modulation-frequency modulation (AM-FM) methods (see Fig. 1f), as it was also proposed by our group in [19]. AM-FM were applied in multiple sclerosis lesions which bear visual similarities with CBM. Out of all the extracted features, only a portion of the most statistically significant ones will be selected for the next step of classification (see Fig. 1h). For the features selection a variety of known methods will be used such as Gini- [13] and random forest-value importance [14]. Finally, the association of the segmented CBM with their primary cancer site will be modeled with both binary and multi-class classifiers such as random forests [13, 14] (see Fig. 1i), and then tested in unseen data (see Fig. 1j), based on the extracted imaging features (see Fig. 1h), along with the location of the CBM in the brain.

## 2.2 Image Dataset

The publicly available CBM image dataset BrainMetShare [20] was used (also used in [11]), which includes MRI scans of in total 156 patients, 105 females and 51 males, of mean age $63 \pm 12$ years old (range: 29 to 92 years), with at least 1 BM per patient and various primary cancers. More specifically, 99 of the patients have CBM originating in lung, 33 in breast, 7 in genitourinary, 5 in gastrointestinal and 5 in miscellaneous cancers. One to three CBM appeared in 64 patients (41%), 4–10 CBM in 47 (30%), while more than 10 CBM appeared in 45 patients (29%). The CBM sizes lie in the range 2–40 mm and were scattered in every region of the brain parenchyma (see also Table 1, Table 2 and Table 3). There are four 3D MRImodalities available, however as a first step in our study, we chose only the T1-weighted gradient-echo post-contrast images. The ground truth CBM masks were manually generated by different radiologists [11] based upon T1-weighted spin-echo pre-contrast images. These masks were compared with those generated automatically by our proposed method. The image masks of the CBM contain binary values (0 for non-CBM, 255 for CBM). All images were resampled to $256 \times 256$ pixels in the transverse plane, with a resolution of 0.94 mm, and were skull-stripped. The resolution in between planes was 1.0 mm. Each 3D image scan contains slices varying in the range of 118 to 286.

<table><caption>**Table 1.** Incidence of primary cancers of CBM included in the dataset used in this study (total patients = 156).</caption><thead><tr><th></th><th></th></tr></thead><tbody><tr><td>Primary cancer: Number of patients (% of total patients)</td><td></td></tr><tr><td>Lung: 99 (63%)</td><td></td></tr><tr><td>Breast: 33 (21%)</td><td></td></tr><tr><td>Melanoma: 7 (5%)</td><td></td></tr><tr><td>Genitourinary: 7 (5%)</td><td></td></tr><tr><td>Gastrointestinal: 5 (3%)</td><td></td></tr><tr><td>Miscellaneous: 5 (3%)</td><td></td></tr></tbody></table>
<table><caption>**Table 2.** Patients' age and CBM range</caption><tr><td>Mean patients' age: 63 ± 12 years old</td></tr><tr><td>Range: 29–92 years</td></tr><tr><td>CBM range in diameter: 2–40 mm</td></tr></table>

<table><caption>**Table 3.** Incidence of CBM with relation to number of CBM per patient.</caption><thead><tr><th>Number of CBM: number of patients (% of total patients)</th></tr></thead><tbody><tr><td>1 to 3: 64 (41%)</td></tr><tr><td>4 to 10: 47 (30%)</td></tr><tr><td>&gt; 10: 45 (29%)</td></tr></tbody></table>

## 2.3 Data Pre-processing

To pre-process the images and prepare them for further processing, we kept only the 2D image slices that contained CBM. We then randomly separated the dataset in training/validation and /testing in a ratio 70/15/15 respectively, thus ending up with 3474/579/579 slices respectively. Moreover, the only pre-processing that the images underwent was their intensity standardization [5, 7, 10] (zero score normalization) per slice, by subtracting the mean and dividing by the standard deviation of each slice. Also, the images of the CBM masks were set to the values 0 and 1 for non-CBM and CBM respectively.

## 2.4 Automated 2D Segmentation Model

The architecture of the 2D modified U-Net used in this study, was also proposed in [16], which was used for the binary brain metastasis segmentation (see also Fig. 2). It consists of six levels, with each level containing two convolution blocks of filter size 3 × 3, a Rectified Linear Unit (ReLU) as an activation function, and dropout layer where a percentage of its input units are randomly set to zero. The dropout rate ranges from 0.1 in the top level to 0.3 in the bottleneck of the network. The downsampling of the last feature maps of each encoding level was performed with max pooling of kernel size 2 × 2. The upsampling in the decoder path was done with transpose convolutions of kernel size 2 × 2. Starting from the input 256 × 256 image, the spatial dimensions got halved in each level until size 8 × 8, while the filters increased from 16 up to 512. The selection of this segmentation network for this study was based on the high accuracy results of U-Nets in brain tumors (gliomas) that can achieve DSC = 0.90 [17]. It was also based on the proposition that generalizable state of the art (as tested on brain tumors - gliomas), can be accomplished by optimizing the training hyper-parameters [18], without the need to develop elaborate architectures.

For the training, the binary cross entropy loss, with the Adam optimizer and learning rate 10⁻⁴ were used. The batch size was set to 10, due to GPU memory limitations. To
determine the optimal number of epochs, the network was trained for 500 epochs and the losses on the training and validation data were noted. It was observed that after the 90th epoch, validation loss was virtually stagnant, therefore the final model presented in this study, was trained for 90 epochs. Furthermore, the threshold to discern between BM and non-BM pixels (to be applied to the predicted probability maps when evaluating the model) was experimentally determined, by evaluating the model on the training data (each slice) with different threshold values, using the DSC metric. The mean of the best DSCs yielded a threshold equal to 0.54 which was chosen. The training and testing of the model was performed on an NVIDIA RTX 3050 GPU (4 GB) and an AMD Ryzen 7 processor. For the training, each epoch was executed in approximately 40 s, thus resulting in 60 min for the whole experiment.

<figure><img src="image_4.png" /><figcaption>Fig. 2. Architecture of the proposed U-Net used in this study [16], which was implemented with the Tensorflow [21]-Keras [22] framework in Python.</figcaption></figure>

## 2.5 Evaluation Metrics

In order to take into account the fact that each MRI slice contains CBM with multiple numbers and sizes, from each ground truth mask we extracted a separate bounding box around the sub-mask for every individual CBM cross section area. Also, for each extracted bounding box, we removed the corresponding area from the predicted mask. Then, the proposed segmentation model was evaluated in the training, validation and testing splits using the metrics SE, SP, PPV, IoU, and DSC [11]. All above metrics were implemented on the corresponding bounding boxes. Moreover, in each ground truth sub-mask, the area of the CBM cross section was calculated for comparison with the segmentation results. The evaluation metrics were calculated separately for every CBM cross section (see Fig. 3 for examples). The results of the metrics are presented both across the entire data (see also Table 2 and Fig. 4) as well as in 10 bins of 96 CBM cross sections each (see also Fig. 5). Below, the definitions of the proposed evaluation metrics are given:

$$SE = \frac{TP}{TP + FN} \qquad (1)$$

$$SP = \frac{TN}{TN + FP} \qquad (2)$$

$$PPV = \frac{TP}{TP + FP} \qquad (3)$$
$$IoU = \frac{TP}{TP + FP + FN} \qquad (4)$$

$$DSC = \frac{2 * TP}{2 * TP + FN + FP} \qquad (5)$$

where TP, TN, FP and FN are the true positives, the true negatives, the false positives and the false negatives, calculated on a pixel basis respectively between the manual performed by an expert and the automated segmentations performed by the proposed method. Furthermore, for evaluating the performance of the segmentation method, box plots were used for graphically demonstrating the distribution of the proposed evaluation metrics (see Fig. 5).

## 3 Results

In Fig. 3, two representative segmentation results are shown, where in image A1, there are nine separate relatively small CBM cross sections ranging from approximately 10 to 30 mm². The segmentation results (see image A2 in Fig. 3), indicate both poor and good performance in this case. This is because the model fails to detect two areas completely, whereas in other areas the model reaches a DSC = 0.88. In Fig. 3, image B1 illustrates two CBM cross sections. In image B2, the automated segmentation results display an area (A = 56 mm²) with a DSC = 0.86, whereas another relatively larger area (A =

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Illustration of two representative examples of automatically segmented brain metastases from the testing dataset. Images are cropped for visual purposes. A1–B1) Images fed to the model. A2–B2) Segmentation results vs ground truths. Next to each cross section of brain metastasis, the DSC is noted in yellow, as calculated per cross section.</figcaption></figure>
511 mm²) gave a DSC = 0.95. In Table 2, the mean values (±standard deviations), and the 25th, 50th and 75th percentiles of the interquartile range (IQR) for all evaluation metrics are presented.

In Fig. 4 we present the results of the evaluation metrics on all images used in this study for comparing our proposed segmentation with the ground truth. Almost all evaluation metrics show a good agreement of the proposed segmentation method with the ground truth. Additionally, the mean values of all evaluation metrics for the different CBM cross section areas from the testing set are displayed in Fig. 5. Each point of the x-axis refers to the smallest of the areas of the CBM cross sections in the corresponding bin (see Fig. 5).

<table><caption>**Table 4.** Mean values with their standard deviations (mean ± std), and IQR percentiles (25th, 50th, 75th) for the segmentation evaluation metrics between the manual vs the automated results.</caption><thead><tr><th>Evaluation metrics</th><th>Training dataset</th><th>Validation dataset</th><th>Testing dataset</th></tr></thead><tbody><tr><td>Sensitivity</td><td>0.85 ± 0.15<br>(0.80, 0.89, 0.95)</td><td>0.71 ± 0.30<br>(0.62, 0.83, 0.93)</td><td>0.70 ± 0.30<br>(0.59, 0.82, 0.93)</td></tr><tr><td>Specificity</td><td>0.73 ± 0.26<br>(0.61, 0.80, 0.92)</td><td>0.76 ± 0.26<br>(0.65, 0.83, 1.00)</td><td>0.77 ± 0.26<br>(0.66, 0.85, 1.00)</td></tr><tr><td>PPV</td><td>0.89 ± 0.10<br>(0.85, 0.92, 0.97)</td><td>0.90 ± 0.10<br>(0.85, 0.92, 0.98)</td><td>0.90 ± 0.11<br>(0.85, 0.92, 0.98)</td></tr><tr><td>IoU</td><td>0.77 ± 0.15<br>(0.70, 0.80, 0.88)</td><td>0.65 ± 0.28<br>(0.57, 0.73, 0.85)</td><td>0.64 ± 0.28<br>(0.53, 0.72, 0.85)</td></tr><tr><td>DSC</td><td>0.86 ± 0.12<br>(0.82, 0.89, 0.94)</td><td>0.74 ± 0.28<br>(0.73, 0.84, 0.92)</td><td>0.73 ± 0.29<br>(0.70, 0.84, 0.92)</td></tr></tbody></table>

IQR: Interquartile range, PPV: Positive Predictive Value, IoU: Intersection over Union, DSC: Dice Similarity Coefficient. IQR shown in parentheses.
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Box plots for the evaluation metrics for the segmentation model across all CBM cross sections in the dataset. Red/Blue/Green boxes: Training/Validation/Testing data: Approximately 5700/900/900 individual cross section areas of CBM. DSC ± std is shown on the right of each box. (Color figure online)</figcaption></figure>

The results of Fig. 5, show a large imbalance in the areas of the CBM cross sections across the dataset, with half of the data being in the range 2.8 mm² to 21.6 mm². The remaining half of the samples lies in the range 21.6 to 1225.7 mm². The mean DSC

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Means of different segmentation evaluation metrics used in this study, for different range of sizes of cross sections of CBM (ground truth), as evaluated in the testing set. The areas were separated to 10 bins of equal number of measurements (N = 96). The displayed areas on the x-axis refer to the left edges. Last bin area = [160.7, 1225.7) mm². In each bin, the mean of each metric is calculated. For visual purposes, the plotted lines are smoothed with a gaussian filter of sigma = 0.75.</figcaption></figure>
values with their standard deviations for each range of CBM cross section areas increase as the area increases too and are as follows: [2.82, 7.52) mm²: 0.55 ± 0.38, [7.52, 10.34) mm²: 0.62 ± 0.33, [10.34, 13.16) mm²: 0.57 ± 0.35, [13.16, 16.92) mm²: 0.63 ± 0.32, [16.92, 21.62) mm²: 0.73 ± 0.21, [21.62, 29.14) mm²: 0.79 ± 0.20, [29.14, 44.18) mm²: 0.77 ± 0.20, [44.18, 76.14) mm²: 0.81 ± 0.21, [76.14, 160.74) mm²: 0.89 ± 0.09, [160.74, 1225.7) mm²: 0.93 ± 0.09.

# 4 Discussion

Brain metastases occur in a large variety with regards to their number, size, shape and location. The diagnosis of brain cancer metastases using MRI scans and the identification of their exact position, shape and origin may contribute to the selection of the appropriate therapy. In this study, we proposed an integrated fully automated system for the segmentation and classification of CBM with regards to their primary site. More specifically, we also evaluated and experimented with our segmentation method as follows. We used a modified U-Net CNN using $N = 3474$ brain image slices for training, $N_v = 579$ for validation and $N_T = 579$ for testing from the public dataset BrainMetShare. The proposed model was evaluated on the testing data ($N_T$), on a lesion-cross section basis with areas from 2.8 to 1225.7 mm² and yielded a $SE = 0.70 \pm 0.30$, a $SP = 0.77 \pm 0.26$, a $PPV = 0.90 \pm 0.11$, an $IoU = 0.64 \pm 0.28$ and a $DSC = 0.73 \pm 0.29$. The present results of this study show the good agreement of the proposed method with the ground truth.

A number of studies have been proposed in the current literature for the automated segmentation and classification of CBM. The current automated state-of-the-art segmentation methods have yielded very good results for brain tumors in general [17], with the U-Net being cited the most and with $DSC = 0.90$ on MRI images. However, the literature in BM segmentation have shown that the necessity for improvements still exists regarding the performance, robustness, generalizability and clinical applicability. The experimental part of this study established the first steps towards the development of an automated segmentation model that will act as the input of the to-be developed automated method for the classification of CBM based on their primary site of cancer.

The used dataset ("BrainMetShare"), contains MRI brain images ranging from 1 to more than 10 CBM per patient and from 2 to 40 mm in diameter which complicated the process of developing an all-around model. More specifically, in the testing dataset, our network produced very good results for BM cross sections from 160.7 to 1225.7 mm² (or theoretically as a reference, from 14.3 to 39.5 mm in diameter – assuming a circular BM). The mean $DSC$ was $0.93 \pm 0.09$. Nevertheless, for areas smaller than $16.92 \text{ mm}^2$ (4.64 mm in diameter) the mean $DSC$ did not pass 0.63. This inadequacy is reported also in [7, 9, 10] for CBM around 5 mm in diameter and smaller. Specifically, in [9] it was reported that any BM smaller than 5 mm is not taken into account in the segmentation evaluation with the $DSC$ metric. Also, it is mentioned that a number of studies in the field of brain disease identification have set a minimum size for the measurable diseases and is equal to 5 mm. Moreover, the mean $DSC$ across the entire testing dataset was $0.73 \pm 0.29$, which paves the way for further enhancement as much in the network architecture as in the optimization of a to-be generalizable preprocessing and training scheme. In the
referenced CBM segmentation studies, all network architectures are different from the 2D U-Net. For comparison, in [4, 5] they used the 3D DeepMedic CNN and reported DSC equal to 0.67 and 0.79. Also, in [7] they used 3D U-Nets in an ensemble with different training datasets and loss functions for each model of the ensemble, resulting in mean DSC = 0.71. In [9, 10] a DSC equal to 0.84 and 0.81 respectively was reported. Furthermore, we report comparable results in this study in DSC as also in [11]. The same MRI dataset as in our study and a fully CNN trained with four MRI modalities in a 3D-to-2D scheme were used, achieving a DSC = 0.79 ± 0.12. However, it is important to note that there is a difference in the calculation of the DSC metrics in our study and the aforementioned studies using 3D models. More specifically in the present study the DSC was averaged across the 2D CBM cross sections, as opposed to the averaged DSC across the CBM volumes of the datasets in the other studies. In all above studies, slightly better results but comparable to our study were reported. This can be due to the fact that different databases, type and number of modalities, pre-processing, data augmentation, post-processing and evaluation metrics criteria in terms of CBM volume were used as compared to our study.

In a future study, conditional random fields (CRF) will be also tested for the segmen-
tation task to investigate if they can further reduce the FP islands of predicted lesions.
Additionally, the proposed method will be further evaluated on a larger number of cases
using additional network parameters, expert observers and evaluation metrics. The model
proposed in this study did not use any elaborate techniques for dataset curation, image
normalization, data augmentation and training schemes. Our next efforts towards these
goals, will include a preprocessing step, where the images will be adaptively cropped in
order to contain as much as brain information as possible in order to create a segmen-
tation network with the smallest possible input patch for memory reasons. Also, data
augmentation techniques such as non-rigid image transformations and simulations of
different noise distributions such as changes in gamma, spatial intensity distributions,
Gaussian and Rician noise, will be tested. Additionally, for datasets that have more than
one modality available, it will be pursued to learn if the excess of such information facil-
itates or confuses the proposed CNN. Also, the multi-modal images will be examined
as separate channels in the network or as a fused single channel. Moreover, a GPU of
higher capacity will be used in order to be able to experiment with larger batch sizes
and the training will be performed on a five-fold cross validation scheme. Finally, for a
more direct comparison with other studies, the evaluation metrics will also be calculated
on a CBM volume basis, where the metrics will be averaged across the volumes of the
dataset. For this to be done, the testing dataset will include contiguous MRI image slices
for every test patient.

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