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| 2. |
- Banerjee, Subhashis, et al.
(författare)
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Deep Curriculum Learning for Follow-up MRI Registration in Glioblastoma
- 2023
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Ingår i: Medical Imaging 2023. - : SPIE -Society of Photo-Optical Instrumentation Engineers. - 9781510660335 - 9781510660342
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Konferensbidrag (refereegranskat)abstract
- This paper presents a weakly supervised deep convolutional neural network-based approach to perform voxel-level3D registration between subsequent follow-up MRI scans of the same patient. To handle the large deformation inthe surrounding brain tissues due to the tumor’s mass effect we proposed curriculum learning-based training forthe network. Weak supervision helps the network to concentrate more focus on the tumor region and resectioncavity through a saliency detection network. Qualitative and quantitative experimental results show the proposedregistration network outperformed two popular state-of-the-art methods.
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| 3. |
- Banerjee, Subhashis, et al.
(författare)
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Lifelong Learning with Dynamic Convolutions for Glioma Segmentation from Multi-Modal MRI
- 2023
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Ingår i: Medical imaging 2023. - : SPIE - International Society for Optical Engineering. - 9781510660335 - 9781510660342
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Konferensbidrag (refereegranskat)abstract
- This paper presents a novel solution for catastrophic forgetting in lifelong learning (LL) using Dynamic Convolution Neural Network (Dy-CNN). The proposed dynamic convolution layer can adapt convolution filters by learning kernel coefficients or weights based on the input image. The suitability of the proposed Dy-CNN in a lifelong sequential learning-based scenario with multi-modal MR images is experimentally demonstrated for the segmentation of Glioma tumors from multi-modal MR images. Experimental results demonstrated the superiority of the Dy-CNN-based segmenting network in terms of learning through multi-modal MRI images and better convergence of lifelong learning-based training.
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| 4. |
- Banerjee, Subhashis, et al.
(författare)
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Segmentation of Intracranial Aneurysm Remnant in MRA using Dual-Attention Atrous Net
- 2021
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Ingår i: 25th International Conference on Pattern Recognition (ICPR). - 9781728188089 ; , s. 9265-9272
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Konferensbidrag (refereegranskat)abstract
- Due to the advancement of non-invasive medical imaging modalities like Magnetic Resonance Angiography (MRA), an increasing number of Intracranial Aneurysm (IA) cases are being reported in recent years. The IAs are typically treated by so-called endovascular coiling, where blood flow in the IA is prevented by embolization with a platinum coil. Accurate quantification of the IA Remnant (IAR), i.e. the volume with blood flow present post treatment is the utmost important factor in choosing the right treatment planning. This is typically done by manually segmenting the aneurysm remnant from the MRA volume. Since manual segmentation of volumetric images is a labour-intensive and error-prone process, development of an automatic volumetric segmentation method is required. Segmentation of small structures such as IA, that may largely vary in size, shape, and location is considered extremely difficult. Similar intensity distribution of IAs and surrounding blood vessels makes it more challenging and susceptible to false positive. In this paper we propose a novel 3D CNN architecture called Dual-Attention Atrous Net (DAtt-ANet), which can efficiently segment IAR volumes from MRA images by reconciling features at different scales using the proposed Parallel Atrous Unit (PAU) along with the use of self-attention mechanism for extracting fine-grained features and intra-class correlation. The proposed DAtt-ANet model is trained and evaluated on a clinical MRA image dataset of IAR consisting of 46 subjects. We compared the proposed DAtt-ANet with five state-of-the-art CNN models based on their segmentation performance. The proposed DAtt-ANet outperformed all other methods and was able to achieve a five-fold cross-validation DICE score of 0.73 +/- 0.06.
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| 5. |
- Banerjee, Subhashis, et al.
(författare)
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Topology-Aware Learning for Volumetric Cerebrovascular Segmentation
- 2022
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Ingår i: 2022 IEEE International Symposium on Biomedical Imaging (IEEE ISBI 2022). - : IEEE. - 9781665429238 ; , s. 1-4
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Konferensbidrag (refereegranskat)abstract
- This paper presents a topology-aware learning strategy for volumetric segmentation of intracranial cerebrovascular structures. We propose a multi-task deep CNN along with a topology-aware loss function for this purpose. Along with the main task (i.e. segmentation), we train the model to learn two related auxiliary tasks viz. learning the distance transform for the voxels on the surface of the vascular tree and learning the vessel centerline. This provides additional regularization and allows the encoder to learn higher-level intermediate representations to boost the performance of the main task. We compare the proposed method with six state-of-the-art deep learning-based 3D vessel segmentation methods, by using a public Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) dataset. Experimental results demonstrate that the proposed method has the best performance in this particular context.
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| 7. |
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| 8. |
- Bengtsson Bernander, Karl, et al.
(författare)
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Rotation-Equivariant Semantic Instance Segmentation on Biomedical Images
- 2022
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Ingår i: Medical Image Understanding and Analysis, MIUA 2022. - Cham : Springer. - 9783031120534 - 9783031120527 ; , s. 283-297
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Konferensbidrag (refereegranskat)abstract
- Advances in image segmentation techniques, brought by convolutional neural network (CNN) architectures like U-Net, show promise for tasks such as automated cancer screening. Recently, these methods have been extended to detect different instances of the same class, which could be used to, for example, characterize individual cells in whole-slide images. Still, the amount of data needed and the number of parameters in the network are substantial. To alleviate these problems, we modify a method of semantic instance segmentation to also enforce equivariance to the p4 symmetry group of 90-degree rotations and translations. We perform four experiments on a synthetic dataset of scattered sticks and a subset of the Kaggle 2018 Data Science Bowl, the BBBC038 dataset, consisting of segmented nuclei images. Results indicate that the rotation-equivariant architecture yields similar accuracy as a baseline architecture. Furthermore, we observe that the rotation-equivariant architecture converges faster than the baseline. This is a promising step towards reducing the training time during development of methods based on deep learning.
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| 9. |
- Bianchi, Kevin, et al.
(författare)
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Dual B-spline Snake for Interactive Myocardial Segmentation
- 2013
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Konferensbidrag (refereegranskat)abstract
- This paper presents a novel interactive segmentation formalism based on two coupledB-Spline snake models to efficiently and simultaneously extract myocardial walls fromshort-axis magnetic resonance images. The main added value of this model is interactionas it is possible to quickly and intuitively correct the result in complex cases withoutrestarting the whole segmentation working flow. During this process, energies computedfrom the images guide the user to the best position of the model.
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| 10. |
- Breznik, Eva, et al.
(författare)
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Effects of distance transform choice in training with boundary loss
- 2021
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Convolutional neural networks are the method of choice for many medical imaging tasks, in particular segmentation. Recently, efforts have been made to include distance measures in the network training, as for example the introduction of boundary loss, calculated via a signed distance transform. Using boundary loss for segmentation can alleviate issues with imbalance and irregular shapes, leading to a better segmentation boundary. It is originally based on the Euclidean distance transform. In this paper we investigate the effects of employing various definitions of distance when using the boundary loss for medical image segmentation. Our results show a promising behaviour in training with non-Euclidean distances, and suggest a possible new use of the boundary loss in segmentation problems.
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