Object Detection Improves Tumour Segmentation in MR Images of Rare Brain Tumours

Chegraoui, Hamza; Philippe, Cathy; Dangouloff‐Ros, Volodia; Grigis, Antoine; Calmon, Raphaël; Boddaert, Nathalie; Frouin, Frédérique; Grill, Jacques; Frouin, Vincent

doi:10.3390/cancers13236113

Cited by 16 publications

(7 citation statements)

References 41 publications

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“…The manual segmentation is however tedious and its reproducibility still needs to be tested. This task is also difficult to automate due to the particularities of DIPG and the difficulties of obtaining a cohort with numerous data (48). Finally, several works remain to be done.…”

Section: Discussionmentioning

confidence: 99%

Multimodal MRI radiomic models to predict genomic mutations in diffuse intrinsic pontine glioma with missing imaging modalities

et al. 2023

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PurposePredicting H3.1, TP53, and ACVR1 mutations in DIPG could aid in the selection of therapeutic options. The contribution of clinical data and multi-modal MRI were studied for these three predictive tasks. To keep the maximum number of subjects, which is essential for a rare disease, missing data were considered. A multi-modal model was proposed, collecting all available data for each patient, without performing any imputation.MethodsA retrospective cohort of 80 patients with confirmed DIPG and at least one of the four MR modalities (T1w, T1c, T2w, and FLAIR), acquired with two different MR scanners was built. A pipeline including standardization of MR data and extraction of radiomic features within the tumor was applied. The values of radiomic features between the two MR scanners were realigned using the ComBat method. For each prediction task, the most robust features were selected based on a recursive feature elimination with cross-validation. Five different models, one based on clinical data and one per MR modality, were developed using logistic regression classifiers. The prediction of the multi-modal model was defined as the average of all possible prediction results among five for each patient. The performances of the models were compared using a leave-one-out approach.ResultsThe percentage of missing modalities ranged from 6 to 11% across modalities and tasks. The performance of each individual model was dependent on each specific task, with an AUC of the ROC curve ranging from 0.63 to 0.80. The multi-modal model outperformed the clinical model for each prediction tasks, thus demonstrating the added value of MRI. Furthermore, regardless of performance criteria, the multi-modal model came in the first place or second place (very close to first). In the leave-one-out approach, the prediction of H3.1 (resp. ACVR1 and TP53) mutations achieved a balanced accuracy of 87.8% (resp. 82.1 and 78.3%).ConclusionCompared with a single modality approach, the multi-modal model combining multiple MRI modalities and clinical features was the most powerful to predict H3.1, ACVR1, and TP53 mutations and provided prediction, even in the case of missing modality. It could be proposed in the absence of a conclusive biopsy.

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Section: Discussionmentioning

confidence: 99%

Multimodal MRI radiomic models to predict genomic mutations in diffuse intrinsic pontine glioma with missing imaging modalities

et al. 2023

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“…The performance of the proposed framework is compared with two different baselines 1 . The first baseline (B1) is the vanilla Faster-RCNN model without any type of modification, as seen in Figure 5.…”

Section: B Implementationmentioning

confidence: 99%

“…I N recent years, there has been a surge of interest in designing new and better Deep Learning (DL) models for critical areas such as healthcare and autonomous driving. The application of these systems in medical imaging analysis has led to the development of a wide variety of functionalities, such as identifying lesions to facilitating diagnoses and prognoses [1]- [3]. Nevertheless, compliance with safety and regulatory standards is imperative for these models to be deployed in medical institutions.…”

Section: Introductionmentioning

confidence: 99%

Intrinsic Explainability for End-to-End Object Detection

Fernandes,

Calado

et al. 2024

IEEE Access

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Deep Learning models are automating many daily routine tasks, indicating that in the future, even high-risk tasks will be automated, such as healthcare and automated driving areas. However, due to the complexity of such deep learning models, it is challenging to understand their reasoning. Furthermore, the black box nature of the designed deep learning models may undermine public confidence in critical areas. Current efforts on intrinsically interpretable models focus only on classification tasks, leaving a gap in models for object detection. Therefore, this paper proposes a deep learning model that is intrinsically explainable for the object detection task. The chosen design for such a model is a combination of the wellknown Faster-RCNN model with the ProtoPNet model. For the Explainable AI experiments, the chosen performance metric was the similarity score from the ProtoPNet model. Our experiments show that this combination leads to a deep learning model that is able to explain its classifications, with similarity scores, using a visual "bag of words", which are called prototypes, that are learned during the training process. Furthermore, the adoption of such an explainable method does not seem to hinder the performance of the proposed model, which achieved a mAP of 69% in the KITTI dataset and a mAP of 66% in the GRAZPEDWRI-DX dataset. Moreover, our explanations have shown a high reliability on the similarity score.

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“…Shelatkar and Bansal compared different variants of YOLOv5 to detect brain tumor location [ 14 ]. Chegraoui et al suggested a model that combines segmentation and detection tasks to find the locations of rare brain tumors [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

In-Domain Transfer Learning Strategy for Tumor Detection on Brain MRI

Terzi

Azginoglu

2023

Diagnostics

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Transfer learning has gained importance in areas where there is a labeled data shortage. However, it is still controversial as to what extent natural image datasets as pre-training sources contribute scientifically to success in different fields, such as medical imaging. In this study, the effect of transfer learning for medical object detection was quantitatively compared using natural and medical image datasets. Within the scope of this study, transfer learning strategies based on five different weight initialization methods were discussed. A natural image dataset MS COCO and brain tumor dataset BraTS 2020 were used as the transfer learning source, and Gazi Brains 2020 was used for the target. Mask R-CNN was adopted as a deep learning architecture for its capability to effectively handle both object detection and segmentation tasks. The experimental results show that transfer learning from the medical image dataset was found to be 10% more successful and showed 24% better convergence performance than the MS COCO pre-trained model, although it contains fewer data. While the effect of data augmentation on the natural image pre-trained model was 5%, the same domain pre-trained model was measured as 2%. According to the most widely used object detection metric, transfer learning strategies using MS COCO weights and random weights showed the same object detection performance as data augmentation. The performance of the most effective strategies identified in the Mask R-CNN model was also tested with YOLOv8. Results showed that even if the amount of data is less than the natural dataset, in-domain transfer learning is more efficient than cross-domain transfer learning. Moreover, this study demonstrates the first use of the Gazi Brains 2020 dataset, which was generated to address the lack of labeled and qualified brain MRI data in the medical field for in-domain transfer learning. Thus, knowledge transfer was carried out from the deep neural network, which was trained with brain tumor data and tested on a different brain tumor dataset.

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Object Detection Improves Tumour Segmentation in MR Images of Rare Brain Tumours

Cited by 16 publications

References 41 publications

Multimodal MRI radiomic models to predict genomic mutations in diffuse intrinsic pontine glioma with missing imaging modalities

Multimodal MRI radiomic models to predict genomic mutations in diffuse intrinsic pontine glioma with missing imaging modalities

Intrinsic Explainability for End-to-End Object Detection

In-Domain Transfer Learning Strategy for Tumor Detection on Brain MRI

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