RatLesNetv2: A Fully Convolutional Network for Rodent Brain Lesion Segmentation

Valverde, Juan Miguel; Shatillo, Artem; Feo, Riccardo De; Gröhn, Olli; Sierra, Alejandra; Tohka, Jussi

doi:10.3389/fnins.2020.610239

Cited by 22 publications

(34 citation statements)

References 34 publications

(48 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where CS = 1 indicates an identical compactness, and lower values indicate the two regions display a different ratio between surface and volume. To calculate the compactness, we used code from ( 27 ).…”

Section: Methodsmentioning

confidence: 99%

“…Alternative approaches include work based on image-to-image translation ( 23 ) or mixed-scale architectures ( 24 ). For murine MRI, neural networks have been proposed for skull-stripping ( 25 , 26 ), lesion segmentation ( 27 , 28 ), and region segmentation ( 29 ). However, to our knowledge, CNNs have not been applied to the task of anatomical region segmentation of MRI of lesioned murine brains.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Convolutional Neural Networks Enable Robust Automatic Segmentation of the Rat Hippocampus in MRI After Traumatic Brain Injury

et al. 2022

Self Cite

View full text Add to dashboard Cite

Registration-based methods are commonly used in the automatic segmentation of magnetic resonance (MR) brain images. However, these methods are not robust to the presence of gross pathologies that can alter the brain anatomy and affect the alignment of the atlas image with the target image. In this work, we develop a robust algorithm, MU-Net-R, for automatic segmentation of the normal and injured rat hippocampus based on an ensemble of U-net-like Convolutional Neural Networks (CNNs). MU-Net-R was trained on manually segmented MR images of sham-operated rats and rats with traumatic brain injury (TBI) by lateral fluid percussion. The performance of MU-Net-R was quantitatively compared with methods based on single and multi-atlas registration using MR images from two large preclinical cohorts. Automatic segmentations using MU-Net-R and multi-atlas registration were of excellent quality, achieving cross-validated Dice scores above 0.90 despite the presence of brain lesions, atrophy, and ventricular enlargement. In contrast, the performance of single-atlas segmentation was unsatisfactory (cross-validated Dice scores below 0.85). Interestingly, the registration-based methods were better at segmenting the contralateral than the ipsilateral hippocampus, whereas MU-Net-R segmented the contralateral and ipsilateral hippocampus equally well. We assessed the progression of hippocampal damage after TBI by using our automatic segmentation tool. Our data show that the presence of TBI, time after TBI, and whether the hippocampus was ipsilateral or contralateral to the injury were the parameters that explained hippocampal volume.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks Enable Robust Automatic Segmentation of the Rat Hippocampus in MRI After Traumatic Brain Injury

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Due to new emerging opportunities, artifi-*tatarkanov@ikti.ru cial NNs have become in high demand in recent years to analyze complex structured images resulting from medical imaging [15][16][17][18]42]. A prime example of a successful application of NNs is the RatLesNetv2 model [19]. The "RatLesNetv2" model is a convolutional neural network that highlights the area of brain damage on a tomographic image.…”

Section: Introductionmentioning

confidence: 99%

“…An important condition of any specialized development is the need to adapt it to specific regional standards and for it to work with the equipment and data formats used by the organization. The relevance of the present work on the automation of ocular diagnostics of fundus pathologies is due to [19] the need to develop domestic medical decision-making systems [21]- [24].The aim of the work is to increase the efficiency of ocular diagnostics of fundus pathologies, to reduce the burden on specialists, and to reduce negative human-factor impacts when making diagnoses by creating and using automated hybrid NN structures for intelligent segmentation of complex structured medical retinal images. The object of the research is an algorithm for noninvasive diagnostics of ocular fundus pathologies based on retinal images obtained by medical imaging techniques.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of neural network structure for the analysis of complex structured ocular fundus images

Tatarkanov¹,

Alexandrov²,

Glashev³

2021

J Appl Eng Science

View full text Add to dashboard Cite

This paper proposes an algorithm for synthesizing a neural network (NN) structure to analyze complex structured, low entropy, ocular fundus images, characterized by iterative tuning of the adaptive model’s solver modules. This algorithm will assist in synthesizing models of NNs that meet the predetermined characteristics of the classification quality. The relevance of automating the process of ocular diagnostics of fundus pathologies is due to the need to develop domestic medical decision-making systems. Because of using the developed algorithm, the NN structure is synthesized, which will include two solver modules, and is intended to classify the dual-alternative information. Automated hybrid NN structures for intelligent segmentation of complex structured, low entropy, retinal images should provide increased efficiency of ocular diagnostics of fundus pathologies, reduce the burden on specialists, and decrease the negative impact of the human factor in diagnosis.

show abstract

“…Accurate segmentation is crucial to measure, for instance, brain tumor size, which can determine the radiation dose administered to patients during radiotherapy [1]. Since segmenting images manually is time consuming and subjective [2,3,4], there is a great interest in developing reliable tools to segment medical images automatically.…”

Section: Introductionmentioning

confidence: 99%

Region-wise Loss for Biomedical Image Segmentation

Valverde¹,

Tohka²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

We propose Region-wise (RW) loss for biomedical image segmentation. Region-wise loss is versatile, can simultaneously account for class imbalance and pixel importance, and it can be easily implemented as the pixel-wise multiplication between the softmax output and a RW map. We show that, under the proposed Region-wise loss framework, certain loss functions, such as Active Contour and Boundary loss, can be reformulated similarly with appropriate RW maps, thus revealing their underlying similarities and a new perspective to understand these loss functions. We investigate the observed optimization instability caused by certain RW maps, such as Boundary loss distance maps, and we introduce a mathematically-grounded principle to avoid such instability. This principle provides excellent adaptability to any dataset and practically ensures convergence without extra regularization terms or optimization tricks. Following this principle, we propose a simple version of boundary distance maps called rectified RW maps that, as we demonstrate in our experiments, achieve state-of-the-art performance with similar or better Dice coefficients and Hausdorff distances than Dice, Focal, and Boundary losses in three distinct segmentation tasks. We quantify the optimization instability provided by Boundary loss distance maps, and we empirically show that our rectified RW maps are stable to optimize. The code to run all our experiments is publicly available at: https://github.com/jmlipman/RegionWiseLoss.

show abstract

RatLesNetv2: A Fully Convolutional Network for Rodent Brain Lesion Segmentation

Cited by 22 publications

References 34 publications

Convolutional Neural Networks Enable Robust Automatic Segmentation of the Rat Hippocampus in MRI After Traumatic Brain Injury

Convolutional Neural Networks Enable Robust Automatic Segmentation of the Rat Hippocampus in MRI After Traumatic Brain Injury

Synthesis of neural network structure for the analysis of complex structured ocular fundus images

Region-wise Loss for Biomedical Image Segmentation

Contact Info

Product

Resources

About