Cristian Axenie, Armin Becher, Daria Kurz, T. Grauschopf
Virtual Reality (VR) sensorimotor rehabilitation is still in infancy but will soon require avatars, digital alter-egos of patients' physical selves. Such embodied interfaces could stimulate patients' perception in a rich and highly customized environment, where sensorimotor deficits, such as in Chemotherapy-Induced Peripheral Neuropathy, could be corrected. In such scenarios, motion prediction is a key ingredient for realistic immersion. Yet, such a task lives under hard processing latency constraints and the inherent variability of human motion. We propose a neural network meta-learning system exploiting the underlying correlations in body kinematics with potential to provide, within latency guarantees, personalized VR rehabilitation. The unsupervised meta-learner is able to extract underlying statistics of the motion data by exploiting data regularities in order to describe the underlying manifold, or structure, of motion under sensorimotor deficits. We demonstrate, through preliminary experiments the potential of such a learning system for adaptive kinematics estimation in personalized rehabilitation VR avatars.
{"title":"Meta-Learning for Avatar Kinematics Reconstruction in Virtual Reality Rehabilitation","authors":"Cristian Axenie, Armin Becher, Daria Kurz, T. Grauschopf","doi":"10.1109/BIBE.2019.00117","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00117","url":null,"abstract":"Virtual Reality (VR) sensorimotor rehabilitation is still in infancy but will soon require avatars, digital alter-egos of patients' physical selves. Such embodied interfaces could stimulate patients' perception in a rich and highly customized environment, where sensorimotor deficits, such as in Chemotherapy-Induced Peripheral Neuropathy, could be corrected. In such scenarios, motion prediction is a key ingredient for realistic immersion. Yet, such a task lives under hard processing latency constraints and the inherent variability of human motion. We propose a neural network meta-learning system exploiting the underlying correlations in body kinematics with potential to provide, within latency guarantees, personalized VR rehabilitation. The unsupervised meta-learner is able to extract underlying statistics of the motion data by exploiting data regularities in order to describe the underlying manifold, or structure, of motion under sensorimotor deficits. We demonstrate, through preliminary experiments the potential of such a learning system for adaptive kinematics estimation in personalized rehabilitation VR avatars.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116153106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lung cancer is the predominant reason for cancer deaths. Deletion mutation (del_E746-A750) in the Epidermal growth factor receptor (EGFR) is liable for 40% of Non-small cell lung cancer (NSCLC). However, 70% of the NSCLC active patients acquire T790M drug resistance mutation after progressing with the first-line EGFR Tyrosine kinase inhibitor (TKI). There are many third-generation EGFR-TKIs which are EGFR mutant selective and produced to heal the patients with T790M resistance mutation. Osimertinib is one of the third-generation EGFR TKIs which irreversibly inhibits the EGFR activity after the T790M mutation. Unfortunately, despite having an impressive initial response, 6 out of 15 patients who were diagnosed with third-generation EGFR-TKI would develop a new resistance and the most frequent being C797S mutation at exon 20. Numerous treatment techniques were implemented for patients who have progressed with C797S second resistance mutation, but there is no official fourth-generation EGFR TKI has invented. In our research work, we analyzed the stability changes for each structure in terms of reduction and hydrogen bonds. Our findings provide insight into the diversity of mechanisms through hydrogen bond analysis in different EGFR structures in terms of stability check and highlight the need for therapeutics and fourth-generation TKI to overcome resistance arbitrated by EGFR C797S.
{"title":"Stability Investigation Using Hydrogen Bonds for Different Mutations and Drug Resistance in Non-Small Cell Lung Cancer Patients","authors":"Avirup Ghosh, Hong Yan","doi":"10.1109/BIBE.2019.00030","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00030","url":null,"abstract":"Lung cancer is the predominant reason for cancer deaths. Deletion mutation (del_E746-A750) in the Epidermal growth factor receptor (EGFR) is liable for 40% of Non-small cell lung cancer (NSCLC). However, 70% of the NSCLC active patients acquire T790M drug resistance mutation after progressing with the first-line EGFR Tyrosine kinase inhibitor (TKI). There are many third-generation EGFR-TKIs which are EGFR mutant selective and produced to heal the patients with T790M resistance mutation. Osimertinib is one of the third-generation EGFR TKIs which irreversibly inhibits the EGFR activity after the T790M mutation. Unfortunately, despite having an impressive initial response, 6 out of 15 patients who were diagnosed with third-generation EGFR-TKI would develop a new resistance and the most frequent being C797S mutation at exon 20. Numerous treatment techniques were implemented for patients who have progressed with C797S second resistance mutation, but there is no official fourth-generation EGFR TKI has invented. In our research work, we analyzed the stability changes for each structure in terms of reduction and hydrogen bonds. Our findings provide insight into the diversity of mechanisms through hydrogen bond analysis in different EGFR structures in terms of stability check and highlight the need for therapeutics and fourth-generation TKI to overcome resistance arbitrated by EGFR C797S.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114153031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Since 2003, when the human reference genome was discovered, several applications found gene sequencing a promising mechanism to help improve their results. These include studying hereditary diseases, prenatal monitoring and others. Gene sequencing in its typical configuration consists of several elaborate processing stages, each performed by a separate software package. The intermediate results are transferred via large files between gene sequencing different steps, hence making gene sequencing a processing and I/O demanding task. Taking advantage of advances memory speed and capacity and with the ultimate goal of pipelining the gene sequencing steps and avoiding utilizing file storage to communicate intermediate results, in this paper we present MemAlign, a novel pre-sorted memory structure to pipeline the first and second processing steps of gene sequencing; Alignment and Sort. The number of memory locations of MemAlign corresponds the positions on the human reference genome. Combined with techniques to compress the alignment results, MemAlign essentially eliminates the Sort step by storing alignment results in the memory location that corresponds to the alignment position. MemAlign not only speeds up the combined processing time of Alignment and Sort, but also saves the considerable amount of storage required to store the intermediate results between the two steps.
{"title":"MemAlign: A Memory Structure to Accelerate Gene Sequencing","authors":"Meysam Roodi, A. Moshovos","doi":"10.1109/BIBE.2019.00036","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00036","url":null,"abstract":"Since 2003, when the human reference genome was discovered, several applications found gene sequencing a promising mechanism to help improve their results. These include studying hereditary diseases, prenatal monitoring and others. Gene sequencing in its typical configuration consists of several elaborate processing stages, each performed by a separate software package. The intermediate results are transferred via large files between gene sequencing different steps, hence making gene sequencing a processing and I/O demanding task. Taking advantage of advances memory speed and capacity and with the ultimate goal of pipelining the gene sequencing steps and avoiding utilizing file storage to communicate intermediate results, in this paper we present MemAlign, a novel pre-sorted memory structure to pipeline the first and second processing steps of gene sequencing; Alignment and Sort. The number of memory locations of MemAlign corresponds the positions on the human reference genome. Combined with techniques to compress the alignment results, MemAlign essentially eliminates the Sort step by storing alignment results in the memory location that corresponds to the alignment position. MemAlign not only speeds up the combined processing time of Alignment and Sort, but also saves the considerable amount of storage required to store the intermediate results between the two steps.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128996854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The electric impedance has been measured to estimate the orientation of cells in vitro. To arrange the orientation, several parallel lines of micro rectangular grooves (1 µm depth, 3 µm width and interval) have been made between a pair of the surface titanium electrodes on the scaffold of glass by the photolithography technique. Two types of arrangements of lines were designed related to the micro grooves: parallel, and perpendicular to the electric line of force between electrodes. C2C12 (mouse myoblast cell line) was cultured on the micro-patterned scaffold. The electric impedance (Z) between electrodes was measured every 24 hours with the sinusoidal electric waves (frequency, 1 kHz < f < 10 MHz; amplitude, v = ± 0.05 V) for three days. The number of oriented cells (Ns) was defined by the number of cells, which cross the straight-line bands of 0.01 mm width connecting between electrodes. The experimental result shows that Z increases with Ns. The phase difference between the voltage and the current is larger at the arrangement of myoblasts oriented in the perpendicular position than that in the parallel position. The present study shows that the measurement of the electric impedance is effective to estimate the orientation of cells.
测定了电阻抗来估计细胞在体外的取向。通过光刻技术，在玻璃支架上的一对表面钛电极之间制作了几条平行线(深度为1µm，宽度为3µm，间距为3µm)的微型矩形凹槽，以排列方向。设计了两种与微凹槽相关的线的排列方式:平行和垂直于电极之间的电力线。小鼠成肌细胞系C2C12在微花纹支架上培养。电极之间的电阻抗(Z)每24小时测量一次，使用正弦波(频率，1 kHz < f < 10 MHz;振幅，v =±0.05 v)三天。取向细胞的数量(Ns)由细胞的数量来定义，这些细胞穿过连接电极之间0.01 mm宽的直线带。实验结果表明，Z随Ns的增大而增大。成肌细胞垂直排列时，电压和电流的相位差比平行排列时大。研究表明，电阻抗的测量是估计细胞取向的有效方法。
{"title":"Monitoring of Orientation of Cells by Electric Impedance: Test on Oriented Cells Using Micro Striped Grooves Pattern by Photolithography","authors":"S. Hashimoto, Kazuyuki Abe","doi":"10.1109/BIBE.2019.00106","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00106","url":null,"abstract":"The electric impedance has been measured to estimate the orientation of cells in vitro. To arrange the orientation, several parallel lines of micro rectangular grooves (1 µm depth, 3 µm width and interval) have been made between a pair of the surface titanium electrodes on the scaffold of glass by the photolithography technique. Two types of arrangements of lines were designed related to the micro grooves: parallel, and perpendicular to the electric line of force between electrodes. C2C12 (mouse myoblast cell line) was cultured on the micro-patterned scaffold. The electric impedance (Z) between electrodes was measured every 24 hours with the sinusoidal electric waves (frequency, 1 kHz < f < 10 MHz; amplitude, v = ± 0.05 V) for three days. The number of oriented cells (Ns) was defined by the number of cells, which cross the straight-line bands of 0.01 mm width connecting between electrodes. The experimental result shows that Z increases with Ns. The phase difference between the voltage and the current is larger at the arrangement of myoblasts oriented in the perpendicular position than that in the parallel position. The present study shows that the measurement of the electric impedance is effective to estimate the orientation of cells.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130483607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
João B. S. Carvalho, José-Maria Moreira, Mário A. T. Figueiredo, Nickolas Papanikolaou
Early detection of lung cancer has shown to significantly improve patient survival. Apart from lesion detection, tumour segmentation is critical for developing radiomics signatures. In this work, we propose a novel hybrid approach for lung lesion detection and segmentation on CT scans, where the segmentation task is assisted by prior detection of regions containing lesions. For the detection task, we introduce a 2.5D residual deep CNN working in a sliding-window fashion, whereas segmentation is tackled by a modified residual U-Net with a weighted-dice plus cross-entropy loss. Experimental results on the LIDC-IDRI dataset and on the lung tumour task dataset within the Medical Segmentation Decathlon show competitive detection performance of the proposed approach (0.902 recall) and superior segmentation capabilities (0.709 dice score). These results confirm the high potential of simpler models, with lower hardware requirements, thus of more general applicability.
{"title":"Automatic Detection and Segmentation of Lung Lesions using Deep Residual CNNs","authors":"João B. S. Carvalho, José-Maria Moreira, Mário A. T. Figueiredo, Nickolas Papanikolaou","doi":"10.1109/BIBE.2019.00182","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00182","url":null,"abstract":"Early detection of lung cancer has shown to significantly improve patient survival. Apart from lesion detection, tumour segmentation is critical for developing radiomics signatures. In this work, we propose a novel hybrid approach for lung lesion detection and segmentation on CT scans, where the segmentation task is assisted by prior detection of regions containing lesions. For the detection task, we introduce a 2.5D residual deep CNN working in a sliding-window fashion, whereas segmentation is tackled by a modified residual U-Net with a weighted-dice plus cross-entropy loss. Experimental results on the LIDC-IDRI dataset and on the lung tumour task dataset within the Medical Segmentation Decathlon show competitive detection performance of the proposed approach (0.902 recall) and superior segmentation capabilities (0.709 dice score). These results confirm the high potential of simpler models, with lower hardware requirements, thus of more general applicability.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129284734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Akshay Gowrishankar, L. Athanasiou, Max L. Olender, E. Edelman
Segmentation of variably differentiated and low-frequency elements in a complex image is challenging. Improving sensitivity demands often prohibitive decreases in specificity. This is particularly the case in intravascular imaging, where detection of heterogeneously dispersed lesion elements, which are often less evident than normal structures, is essential. Modalities including optical coherence tomography (OCT) provide cross-sectional images of coronary arteries that reveal atherosclerotic plaques. Manual plaque segmentation is time consuming and error prone; automated methods are quicker but dictate accuracy tradeoffs. We developed a neural network-based method for automatic detection of calcified plaques in OCT images using texture-based features and examined how underlying training data distribution impacts sensitivity and predictive value. The method assesses each pixel, rather than a patch, as an independent unit, enabling precise control of training data distribution while simultaneously decreasing reliance on massive imaging datasets for training. Pixels from 30 manually annotated OCT images of calcified plaques were used to train the neural network. Several texture measures were computed for the local neighborhood of each pixel and used as inputs to a multi-layered neural network. The ratio of pixels of each class in the training dataset was then varied and the resulting network performance was compared. Positive predictive value and sensitivity ranged from 0.69 to 0.77 and 0.35 to 0.86, respectively, as the ratio of non-calcified to calcified pixels varied from around 15 to 1, with inverse changes in specificity. The results clearly demonstrate that appropriately balanced data must be carefully curated with thoughtful consideration of the model's application and the clinical imperative being addressed.
{"title":"Neural Network Training Data Profoundly Impacts Texture-Based Intravascular Image Segmentation","authors":"Akshay Gowrishankar, L. Athanasiou, Max L. Olender, E. Edelman","doi":"10.1109/BIBE.2019.00184","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00184","url":null,"abstract":"Segmentation of variably differentiated and low-frequency elements in a complex image is challenging. Improving sensitivity demands often prohibitive decreases in specificity. This is particularly the case in intravascular imaging, where detection of heterogeneously dispersed lesion elements, which are often less evident than normal structures, is essential. Modalities including optical coherence tomography (OCT) provide cross-sectional images of coronary arteries that reveal atherosclerotic plaques. Manual plaque segmentation is time consuming and error prone; automated methods are quicker but dictate accuracy tradeoffs. We developed a neural network-based method for automatic detection of calcified plaques in OCT images using texture-based features and examined how underlying training data distribution impacts sensitivity and predictive value. The method assesses each pixel, rather than a patch, as an independent unit, enabling precise control of training data distribution while simultaneously decreasing reliance on massive imaging datasets for training. Pixels from 30 manually annotated OCT images of calcified plaques were used to train the neural network. Several texture measures were computed for the local neighborhood of each pixel and used as inputs to a multi-layered neural network. The ratio of pixels of each class in the training dataset was then varied and the resulting network performance was compared. Positive predictive value and sensitivity ranged from 0.69 to 0.77 and 0.35 to 0.86, respectively, as the ratio of non-calcified to calcified pixels varied from around 15 to 1, with inverse changes in specificity. The results clearly demonstrate that appropriately balanced data must be carefully curated with thoughtful consideration of the model's application and the clinical imperative being addressed.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123530054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Andreas K Triantafyllidis, Anastasios Alexiadis, Dimosthenis Elmas, K. Votis, D. Tzovaras
Childhood obesity is a major public health concern since it is associated with obesity during adulthood and the occurrence of diseases such as diabetes and cancer. In this paper we present a platform for childhood obesity prevention, which is based on interactive games played between a social robot and the child. The interactions between the robot and the child occur within four distinct stages: a) Familiarization with the robot, b) education and knowledge assessment (e.g., for a balanced diet), c) behavioural data collection & empowerment, and d) personalized goal-setting. The platform was evaluated in terms of usability with 10 participants achieving a System Usability Score (SUS) of 93.8. Overall, our results show the feasibility of the adopted approach and the potential of social robots in playing a major role for promoting healthy behaviours and preventing childhood obesity.
{"title":"A Social Robot-Based Platform for Prevention of Childhood Obesity","authors":"Andreas K Triantafyllidis, Anastasios Alexiadis, Dimosthenis Elmas, K. Votis, D. Tzovaras","doi":"10.1109/BIBE.2019.00171","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00171","url":null,"abstract":"Childhood obesity is a major public health concern since it is associated with obesity during adulthood and the occurrence of diseases such as diabetes and cancer. In this paper we present a platform for childhood obesity prevention, which is based on interactive games played between a social robot and the child. The interactions between the robot and the child occur within four distinct stages: a) Familiarization with the robot, b) education and knowledge assessment (e.g., for a balanced diet), c) behavioural data collection & empowerment, and d) personalized goal-setting. The platform was evaluated in terms of usability with 10 participants achieving a System Usability Score (SUS) of 93.8. Overall, our results show the feasibility of the adopted approach and the potential of social robots in playing a major role for promoting healthy behaviours and preventing childhood obesity.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116284935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. D. V. Garcia, E. Leiss, M. Karkoub, P. Tsiamyrtzis, N. Tsekos, N. Navkar, S. Balakrishnan, J. Abinahed, A. Al-Ansari, Georges Younes, A. Darweesh, Khalid Al-Rumaihi, E. Christoforou
Prostate biopsy is considered as a definitive way for diagnosing prostate malignancies. Urologists are currently moving towards MR-guided prostate biopsies over conventional transrectal ultrasound-guided biopsies for prostate cancer detection. Recently, robotic systems have started to emerge as an assistance tool for urologists to perform MR-guided prostate biopsies. However, these robotic assistance systems are designed for a specific clinical environment and cannot be adapted to modifications or changes applied to the clinical setting and/or workflow. This work presents the preliminary design of a cable-driven manipulator developed to be used in both MR scanners and MR-ultrasound fusion systems. The proposed manipulator design and functionality are evaluated on a simulated virtual environment. The simulation is created on an in-house developed interventional planning software to evaluate the ergonomics and usability. The results show that urologists can benefit from the proposed design of the manipulator and planning software to accurately perform biopsies of targeted areas in the prostate.
{"title":"Preliminary Evaluation of Robotic Transrectal Biopsy System on an Interventional Planning Software","authors":"J. D. V. Garcia, E. Leiss, M. Karkoub, P. Tsiamyrtzis, N. Tsekos, N. Navkar, S. Balakrishnan, J. Abinahed, A. Al-Ansari, Georges Younes, A. Darweesh, Khalid Al-Rumaihi, E. Christoforou","doi":"10.1109/BIBE.2019.00070","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00070","url":null,"abstract":"Prostate biopsy is considered as a definitive way for diagnosing prostate malignancies. Urologists are currently moving towards MR-guided prostate biopsies over conventional transrectal ultrasound-guided biopsies for prostate cancer detection. Recently, robotic systems have started to emerge as an assistance tool for urologists to perform MR-guided prostate biopsies. However, these robotic assistance systems are designed for a specific clinical environment and cannot be adapted to modifications or changes applied to the clinical setting and/or workflow. This work presents the preliminary design of a cable-driven manipulator developed to be used in both MR scanners and MR-ultrasound fusion systems. The proposed manipulator design and functionality are evaluated on a simulated virtual environment. The simulation is created on an in-house developed interventional planning software to evaluate the ergonomics and usability. The results show that urologists can benefit from the proposed design of the manipulator and planning software to accurately perform biopsies of targeted areas in the prostate.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121562664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Blood vessels are responsible for the distribution of nutrients and oxygen to all cells in the human body. Due to advances in technological research, many medical applications that use 3d objects are being developed to assist specialists. With the aim of increasing the physical realism of 3D simulations in this scenario, our approach combines Smooth Particle hydrodynamics methods and mass-spring to simulate elastic deformations in the blood vessels during the circulation of the blood. The results, although partial, indicate that the deformation visualization is compatible with the expected physical behavior in a real situation. Additionally, we also observed that the behavior of the flow is different when considering a vessel with rigid walls, which is the case most of the related work.
{"title":"Blood Flow SPH Simulation with Elastic Deformation of Blood Vessels","authors":"Antonio Deusany de Carvalho Junior, H. Bíscaro","doi":"10.1109/BIBE.2019.00102","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00102","url":null,"abstract":"Blood vessels are responsible for the distribution of nutrients and oxygen to all cells in the human body. Due to advances in technological research, many medical applications that use 3d objects are being developed to assist specialists. With the aim of increasing the physical realism of 3D simulations in this scenario, our approach combines Smooth Particle hydrodynamics methods and mass-spring to simulate elastic deformations in the blood vessels during the circulation of the blood. The results, although partial, indicate that the deformation visualization is compatible with the expected physical behavior in a real situation. Additionally, we also observed that the behavior of the flow is different when considering a vessel with rigid walls, which is the case most of the related work.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131393103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cancer subtypes identification is very important for the advancement of precision cancer disease diagnosis and therapy. It is one of the important components of the personalized medicine framework. Cervical cancer (CC) is one of the leading gynecological cancers that causes deaths in women worldwide. However, there is a lack of studies to identify histological subtypes among the patients suffering from tumor of the uterine cervix. Hence, sub-typing of cancer can help in analyzing shared molecular profiles between different histological subtypes of solid tumors of uterine cervix. With the advancement in technology, large scale multi-omics data are generated. The integration of genomics data generated from different platforms helps in capturing complementary information about the patients. Several computational approaches have been developed that integrate muti-omics data for cancer sub-typing. In this study, mRNA (messenger RNA) and miRNA (microRNA) expression data are integrated to identify the histological subtypes of CC. In this regard, a method is proposed that ranks the biomarkers (mRNA and miRNA) on the basis of their varying expression across the samples. The ranking method generates a weight for every biomarker which is further used to identify the similarity between the samples. A well-known approach named Similarity Network Fusion (SNF) is then applied, followed by Spectral clustering, to identify groups of related samples. This study focuses on the role of weighing the biomarkers prior to their integration and application of the clustering algorithm. The weighing method proposed in this study is compared with some other methods and proved to be more efficient. The proposed method helps in identifying histological subtypes of CC and can also be applied to other types of cancer data where histological subtypes play a key role in designing treatments and therapies.
{"title":"Importance of Feature Weighing in Cervical Cancer Subtypes Identification","authors":"Madhumita Madhumita, Sushmita Paul","doi":"10.1109/BIBE.2019.00024","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00024","url":null,"abstract":"Cancer subtypes identification is very important for the advancement of precision cancer disease diagnosis and therapy. It is one of the important components of the personalized medicine framework. Cervical cancer (CC) is one of the leading gynecological cancers that causes deaths in women worldwide. However, there is a lack of studies to identify histological subtypes among the patients suffering from tumor of the uterine cervix. Hence, sub-typing of cancer can help in analyzing shared molecular profiles between different histological subtypes of solid tumors of uterine cervix. With the advancement in technology, large scale multi-omics data are generated. The integration of genomics data generated from different platforms helps in capturing complementary information about the patients. Several computational approaches have been developed that integrate muti-omics data for cancer sub-typing. In this study, mRNA (messenger RNA) and miRNA (microRNA) expression data are integrated to identify the histological subtypes of CC. In this regard, a method is proposed that ranks the biomarkers (mRNA and miRNA) on the basis of their varying expression across the samples. The ranking method generates a weight for every biomarker which is further used to identify the similarity between the samples. A well-known approach named Similarity Network Fusion (SNF) is then applied, followed by Spectral clustering, to identify groups of related samples. This study focuses on the role of weighing the biomarkers prior to their integration and application of the clustering algorithm. The weighing method proposed in this study is compared with some other methods and proved to be more efficient. The proposed method helps in identifying histological subtypes of CC and can also be applied to other types of cancer data where histological subtypes play a key role in designing treatments and therapies.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133390781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：