| 1. |
|
|
| 2. |
- Agarwala, Sunita, et al.
(författare)
-
Convolutional Neural Networks for Efficient Localization of Interstitial Lung Disease Patterns in HRCT Images
- 2018
-
Ingår i: Medical Image Understanding and Analysis. - Cham : Springer Nature. - 9783319959214 - 9783319959207 ; , s. 12-22
-
Konferensbidrag (refereegranskat)abstract
- Lung field segmentation is the first step towards the development of any computer aided diagnosis (CAD) system for interstitial lung diseases (ILD) observed in chest high resolution computed tomography (HRCT) images. If the segmentation is not done efficiently it will compromise the accuracy of CAD system. In this paper, a deep learning-based method is proposed to localize several interstitial lung disease patterns (ILD) in HRCT images without performing lung field segmentation. In this paper, localization of several ILD patterns is performed in image slice. The pretrained models of ZF and VGG networks were fine-tuned in order to localize ILD patterns using Faster R-CNN framework. The three most difficult ILD patterns consolidation, emphysema, and fibrosis have been used for this study and the accuracy of the method has been evaluated in terms of mean average precision (mAP) and free receiver operating characteristic (FROC) curve. The model achieved mAP value of 75% and 83% on ZF and VGG networks, respectively. The result obtained shows the effectiveness of the method in the localization of different ILD patterns.
|
|
| 3. |
- Kumar, Abhishek, et al.
(författare)
-
Segmentation of Lung Field in HRCT Images Using U-Net Based Fully Convolutional Networks
- 2018
-
Ingår i: Medical Image Understanding and Analysis. - Cham : Springer Nature. - 9783319959214 - 9783319959207 ; , s. 84-93
-
Konferensbidrag (refereegranskat)abstract
- Segmentation is a preliminary step towards the development of automated computer aided diagnosis system (CAD). The system accuracy and efficiency primarily depend on the accurate segmentation result. Effective lung field segmentation is major challenging task, especially in the presence of different types of interstitial lung diseases (ILD). At present, high resolution computed tomography (HRCT) is considered to be the best imaging modality to observe ILD patterns. The most common patterns based on their textural appearances are consolidation, emphysema, fibrosis, ground glass opacity (GGO), reticulation and micronodules. In this paper, automatic lung field segmentation of pathological lung has been done using U-Net based deep convolutional networks. Our proposed model has been evaluated on publicly available MedGIFT database. The segmentation result was evaluated in terms of the dice similarity coefficient (DSC). Finally, the experimental results obtained on 330 testing images of different patterns achieving 94% of average DSC.
|
|
| 4. |
- Azharuddin, Mohammad, et al.
(författare)
-
Dissecting multi drug resistance in head and neck cancer cells using multicellular tumor spheroids
- 2019
-
Ingår i: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 9
-
Tidskriftsartikel (refereegranskat)abstract
- One of the hallmarks of cancers is their ability to develop resistance against therapeutic agents. Therefore, developing effective in vitro strategies to identify drug resistance remains of paramount importance for successful treatment. One of the ways cancer cells achieve drug resistance is through the expression of efflux pumps that actively pump drugs out of the cells. To date, several studies have investigated the potential of using 3-dimensional (3D) multicellular tumor spheroids (MCSs) to assess drug resistance; however, a unified system that uses MCSs to differentiate between multi drug resistance (MDR) and non-MDR cells does not yet exist. In the present report we describe MCSs obtained from post-diagnosed, pre-treated patient-derived (PTPD) cell lines from head and neck squamous cancer cells (HNSCC) that often develop resistance to therapy. We employed an integrated approach combining response to clinical drugs and screening cytotoxicity, monitoring real-time drug uptake, and assessing transporter activity using flow cytometry in the presence and absence of their respective specific inhibitors. The report shows a comparative response to MDR, drug efflux capability and reactive oxygen species (ROS) activity to assess the resistance profile of PTPD MCSs and two-imensional (2D) monolayer cultures of the same set of cell lines. We show that MCSs provide a robust and reliable in vitro model to evaluate clinical relevance. Our proposed strategy can also be clinically applicable for profiling drug resistance in cancers with unknown resistance profiles, which consequently can indicate benefit from downstream therapy.
|
|
| 5. |
|
|
| 6. |
- Patra, Hirak Kumar, 1981-, et al.
(författare)
-
Rational Nanotoolbox with Theranostic Potential for Medicated Pro-Regenerative Corneal Implants
- 2019
-
Ingår i: Advanced Functional Materials. - : John Wiley & Sons. - 1616-301X .- 1616-3028. ; 29:38
-
Tidskriftsartikel (refereegranskat)abstract
- Cornea diseases are a leading cause of blindness and the disease burden is exacerbated by the increasing shortage around the world for cadaveric donor corneas. Despite the advances in the field of regenerative medicine, successful transplantation of laboratory‐made artificial corneas is not fully realized in clinical practice. The causes of failure of such artificial corneal implants are multifactorial and include latent infections from viruses and other microbes, enzyme overexpression, implant degradation, extrusion or delayed epithelial regeneration. Therefore, there is an urgent unmet need for developing customized corneal implants to suit the host environment and counter the effects of inflammation or infection, which are able to track early signs of implant failure in situ. This work reports a nanotoolbox comprising tools for protection from infection, promotion of regeneration, and noninvasive monitoring of the in situ corneal environment. These nanosystems can be incorporated within pro‐regenerative biosynthetic implants, transforming them into theranostic devices, which are able to respond to biological changes following implantation.
|
|
| 7. |
- Zhu, Geyunjian H., et al.
(författare)
-
Feasibility of Coacervate-Like Nanostructure for Instant Drug Nanoformulation
- 2023
-
Ingår i: ACS Applied Materials and Interfaces. - : AMER CHEMICAL SOC. - 1944-8244 .- 1944-8252. ; 15:14, s. 17485-17494
-
Tidskriftsartikel (refereegranskat)abstract
- Despite the enormous advancements in nanomedicine research, a limited number of nanoformulations are available on the market, and few have been translated to clinics. An easily scalable, sustainable, and cost-effective manufacturing strategy and long-term stability for storage are crucial for successful translation. Here, we report a system and method to instantly formulate NF achieved with a nanoscale polyelectrolyte coacervate-like system, consisting of anionic pseudopeptide poly(L-lysine isophthalamide) derivatives, polyethylenimine, and doxorubicin (Dox) via simple "mix-and-go" addition of precursor solutions in seconds. The coacervate-like nanosystem shows enhanced intracellular delivery of Dox to patient-derived multidrug-resistant (MDR) cells in 3D tumor spheroids. The results demonstrate the feasibility of an instant drug formulation using a coacervate-like nanosystem. We envisage that this technique can be widely utilized in the nanomedicine field to bypass the special requirement of large-scale production and elongated shelf life of nanomaterials.
|
|
| 8. |
- Banerjee, Subhashis, et al.
(författare)
-
Segmentation of Intracranial Aneurysm Remnant in MRA using Dual-Attention Atrous Net
- 2021
-
Ingår i: 25th International Conference on Pattern Recognition (ICPR). - 9781728188089 ; , s. 9265-9272
-
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.
|
|
| 9. |
- Banerjee, Subhashis, et al.
(författare)
-
Streamlining neuroradiology workflow with AI for improved cerebrovascular structure monitoring
- 2024
-
Ingår i: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 14:1
-
Tidskriftsartikel (refereegranskat)abstract
- Radiological imaging to examine intracranial blood vessels is critical for preoperative planning and postoperative follow-up. Automated segmentation of cerebrovascular anatomy from Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) can provide radiologists with a more detailed and precise view of these vessels. This paper introduces a domain generalized artificial intelligence (AI) solution for volumetric monitoring of cerebrovascular structures from multi-center MRAs. Our approach utilizes a multi-task deep convolutional neural network (CNN) with a topology-aware loss function to learn voxel-wise segmentation of the cerebrovascular tree. We use Decorrelation Loss to achieve domain regularization for the encoder network and auxiliary tasks to provide additional regularization and enable the encoder to learn higher-level intermediate representations for improved performance. We compare our method to six state-of-the-art 3D vessel segmentation methods using retrospective TOF-MRA datasets from multiple private and public data sources scanned at six hospitals, with and without vascular pathologies. The proposed model achieved the best scores in all the qualitative performance measures. Furthermore, we have developed an AI-assisted Graphical User Interface (GUI) based on our research to assist radiologists in their daily work and establish a more efficient work process that saves time.
|
|
| 10. |
- Banerjee, Subhashis, et al.
(författare)
-
Topology-Aware Learning for Volumetric Cerebrovascular Segmentation
- 2022
-
Ingår i: 2022 IEEE International Symposium on Biomedical Imaging (IEEE ISBI 2022). - : IEEE. - 9781665429238 ; , s. 1-4
-
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.
|
|
