Deep Learning based Vertebral Body Segmentation with Extraction of Spinal Measurements and Disorder Disease Classification
Assessment of medical images and diagnostic decision making of lumbar associated diseases by clinicians is invariably subjective, time consuming and challenging task. Presently, clinicians make use of either manual or semi-automated computer-aided tools to make relevant measurements for adding vote...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Masood, Rao Farhat [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Independent influences of excessive body weight and elevated blood pressure from childhood on left ventricular geometric remodeling in adulthood - Yan, Yinkun ELSEVIER, 2017, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:71 ; year:2022 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.bspc.2021.103230 |
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ELV055775764 |
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| 520 | |a Assessment of medical images and diagnostic decision making of lumbar associated diseases by clinicians is invariably subjective, time consuming and challenging task. Presently, clinicians make use of either manual or semi-automated computer-aided tools to make relevant measurements for adding vote of confidence to their grading and evaluation. Lacking reliability and offering substantive dissimilarity once performed by different clinicians, these methods complicate the evaluation process. In an effort to support the decision making process of clinicians, in this paper we present a lumbar assessment framework with autonomous extraction of spinal measurements. Furthermore, an effort is made to address the challenges faced by clinicians while assessing disorders including spondylolisthesis and assessment of lumbar lordosis (LL) by proposing novel disease classification methodologies. For spondylolisthesis classification, we achieved an accuracy of 89% by using angular deviation metric whereas, 93% accuracy for determining adequacy/inadequacy in LL assessment through computation of area within enclosed lumbar curve region. Our framework involves semantic segmentation of vertebral bodies (VBs) using ResNet-UNet where we achieved DSC of 0.97 and IoU of 0.86. Subsequently, we achieved a statistically significant correlation coefficient R and encouraging mean absolute error (MAE) with clinicians’ grading for measurements involving lumbar lordotic angle (LLA), lumbosacral angle (LSA), VB dimensions and lumbar height. In addition to this, we have publicly released the dataset with all the clinicians markings at https://data.mendeley.com/datasets/k3b363f3vz/2. | ||
| 520 | |a Assessment of medical images and diagnostic decision making of lumbar associated diseases by clinicians is invariably subjective, time consuming and challenging task. Presently, clinicians make use of either manual or semi-automated computer-aided tools to make relevant measurements for adding vote of confidence to their grading and evaluation. Lacking reliability and offering substantive dissimilarity once performed by different clinicians, these methods complicate the evaluation process. In an effort to support the decision making process of clinicians, in this paper we present a lumbar assessment framework with autonomous extraction of spinal measurements. Furthermore, an effort is made to address the challenges faced by clinicians while assessing disorders including spondylolisthesis and assessment of lumbar lordosis (LL) by proposing novel disease classification methodologies. For spondylolisthesis classification, we achieved an accuracy of 89% by using angular deviation metric whereas, 93% accuracy for determining adequacy/inadequacy in LL assessment through computation of area within enclosed lumbar curve region. Our framework involves semantic segmentation of vertebral bodies (VBs) using ResNet-UNet where we achieved DSC of 0.97 and IoU of 0.86. Subsequently, we achieved a statistically significant correlation coefficient R and encouraging mean absolute error (MAE) with clinicians’ grading for measurements involving lumbar lordotic angle (LLA), lumbosacral angle (LSA), VB dimensions and lumbar height. In addition to this, we have publicly released the dataset with all the clinicians markings at https://data.mendeley.com/datasets/k3b363f3vz/2. | ||
| 650 | 7 | |a Lumbar spine dataset |2 Elsevier | |
| 650 | 7 | |a Automated disease classification |2 Elsevier | |
| 650 | 7 | |a Spinal measurements |2 Elsevier | |
| 700 | 1 | |a Taj, Imtiaz Ahmad |4 oth | |
| 700 | 1 | |a Khan, Muhammad Babar |4 oth | |
| 700 | 1 | |a Qureshi, Muhammad Asad |4 oth | |
| 700 | 1 | |a Hassan, Taimur |4 oth | |
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10.1016/j.bspc.2021.103230 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001583.pica (DE-627)ELV055775764 (ELSEVIER)S1746-8094(21)00827-2 DE-627 ger DE-627 rakwb eng 610 VZ 630 640 610 VZ Masood, Rao Farhat verfasserin aut Deep Learning based Vertebral Body Segmentation with Extraction of Spinal Measurements and Disorder Disease Classification 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Assessment of medical images and diagnostic decision making of lumbar associated diseases by clinicians is invariably subjective, time consuming and challenging task. Presently, clinicians make use of either manual or semi-automated computer-aided tools to make relevant measurements for adding vote of confidence to their grading and evaluation. Lacking reliability and offering substantive dissimilarity once performed by different clinicians, these methods complicate the evaluation process. In an effort to support the decision making process of clinicians, in this paper we present a lumbar assessment framework with autonomous extraction of spinal measurements. Furthermore, an effort is made to address the challenges faced by clinicians while assessing disorders including spondylolisthesis and assessment of lumbar lordosis (LL) by proposing novel disease classification methodologies. For spondylolisthesis classification, we achieved an accuracy of 89% by using angular deviation metric whereas, 93% accuracy for determining adequacy/inadequacy in LL assessment through computation of area within enclosed lumbar curve region. Our framework involves semantic segmentation of vertebral bodies (VBs) using ResNet-UNet where we achieved DSC of 0.97 and IoU of 0.86. Subsequently, we achieved a statistically significant correlation coefficient R and encouraging mean absolute error (MAE) with clinicians’ grading for measurements involving lumbar lordotic angle (LLA), lumbosacral angle (LSA), VB dimensions and lumbar height. In addition to this, we have publicly released the dataset with all the clinicians markings at https://data.mendeley.com/datasets/k3b363f3vz/2. Assessment of medical images and diagnostic decision making of lumbar associated diseases by clinicians is invariably subjective, time consuming and challenging task. Presently, clinicians make use of either manual or semi-automated computer-aided tools to make relevant measurements for adding vote of confidence to their grading and evaluation. Lacking reliability and offering substantive dissimilarity once performed by different clinicians, these methods complicate the evaluation process. In an effort to support the decision making process of clinicians, in this paper we present a lumbar assessment framework with autonomous extraction of spinal measurements. Furthermore, an effort is made to address the challenges faced by clinicians while assessing disorders including spondylolisthesis and assessment of lumbar lordosis (LL) by proposing novel disease classification methodologies. For spondylolisthesis classification, we achieved an accuracy of 89% by using angular deviation metric whereas, 93% accuracy for determining adequacy/inadequacy in LL assessment through computation of area within enclosed lumbar curve region. Our framework involves semantic segmentation of vertebral bodies (VBs) using ResNet-UNet where we achieved DSC of 0.97 and IoU of 0.86. Subsequently, we achieved a statistically significant correlation coefficient R and encouraging mean absolute error (MAE) with clinicians’ grading for measurements involving lumbar lordotic angle (LLA), lumbosacral angle (LSA), VB dimensions and lumbar height. In addition to this, we have publicly released the dataset with all the clinicians markings at https://data.mendeley.com/datasets/k3b363f3vz/2. Lumbar spine dataset Elsevier Automated disease classification Elsevier Spinal measurements Elsevier Taj, Imtiaz Ahmad oth Khan, Muhammad Babar oth Qureshi, Muhammad Asad oth Hassan, Taimur oth Enthalten in Elsevier Yan, Yinkun ELSEVIER Independent influences of excessive body weight and elevated blood pressure from childhood on left ventricular geometric remodeling in adulthood 2017 Amsterdam [u.a.] (DE-627)ELV020088493 volume:71 year:2022 pages:0 https://doi.org/10.1016/j.bspc.2021.103230 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_60 AR 71 2022 0 |
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10.1016/j.bspc.2021.103230 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001583.pica (DE-627)ELV055775764 (ELSEVIER)S1746-8094(21)00827-2 DE-627 ger DE-627 rakwb eng 610 VZ 630 640 610 VZ Masood, Rao Farhat verfasserin aut Deep Learning based Vertebral Body Segmentation with Extraction of Spinal Measurements and Disorder Disease Classification 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Assessment of medical images and diagnostic decision making of lumbar associated diseases by clinicians is invariably subjective, time consuming and challenging task. Presently, clinicians make use of either manual or semi-automated computer-aided tools to make relevant measurements for adding vote of confidence to their grading and evaluation. Lacking reliability and offering substantive dissimilarity once performed by different clinicians, these methods complicate the evaluation process. In an effort to support the decision making process of clinicians, in this paper we present a lumbar assessment framework with autonomous extraction of spinal measurements. Furthermore, an effort is made to address the challenges faced by clinicians while assessing disorders including spondylolisthesis and assessment of lumbar lordosis (LL) by proposing novel disease classification methodologies. For spondylolisthesis classification, we achieved an accuracy of 89% by using angular deviation metric whereas, 93% accuracy for determining adequacy/inadequacy in LL assessment through computation of area within enclosed lumbar curve region. Our framework involves semantic segmentation of vertebral bodies (VBs) using ResNet-UNet where we achieved DSC of 0.97 and IoU of 0.86. Subsequently, we achieved a statistically significant correlation coefficient R and encouraging mean absolute error (MAE) with clinicians’ grading for measurements involving lumbar lordotic angle (LLA), lumbosacral angle (LSA), VB dimensions and lumbar height. In addition to this, we have publicly released the dataset with all the clinicians markings at https://data.mendeley.com/datasets/k3b363f3vz/2. Assessment of medical images and diagnostic decision making of lumbar associated diseases by clinicians is invariably subjective, time consuming and challenging task. Presently, clinicians make use of either manual or semi-automated computer-aided tools to make relevant measurements for adding vote of confidence to their grading and evaluation. Lacking reliability and offering substantive dissimilarity once performed by different clinicians, these methods complicate the evaluation process. In an effort to support the decision making process of clinicians, in this paper we present a lumbar assessment framework with autonomous extraction of spinal measurements. Furthermore, an effort is made to address the challenges faced by clinicians while assessing disorders including spondylolisthesis and assessment of lumbar lordosis (LL) by proposing novel disease classification methodologies. For spondylolisthesis classification, we achieved an accuracy of 89% by using angular deviation metric whereas, 93% accuracy for determining adequacy/inadequacy in LL assessment through computation of area within enclosed lumbar curve region. Our framework involves semantic segmentation of vertebral bodies (VBs) using ResNet-UNet where we achieved DSC of 0.97 and IoU of 0.86. Subsequently, we achieved a statistically significant correlation coefficient R and encouraging mean absolute error (MAE) with clinicians’ grading for measurements involving lumbar lordotic angle (LLA), lumbosacral angle (LSA), VB dimensions and lumbar height. In addition to this, we have publicly released the dataset with all the clinicians markings at https://data.mendeley.com/datasets/k3b363f3vz/2. Lumbar spine dataset Elsevier Automated disease classification Elsevier Spinal measurements Elsevier Taj, Imtiaz Ahmad oth Khan, Muhammad Babar oth Qureshi, Muhammad Asad oth Hassan, Taimur oth Enthalten in Elsevier Yan, Yinkun ELSEVIER Independent influences of excessive body weight and elevated blood pressure from childhood on left ventricular geometric remodeling in adulthood 2017 Amsterdam [u.a.] (DE-627)ELV020088493 volume:71 year:2022 pages:0 https://doi.org/10.1016/j.bspc.2021.103230 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_60 AR 71 2022 0 |
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10.1016/j.bspc.2021.103230 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001583.pica (DE-627)ELV055775764 (ELSEVIER)S1746-8094(21)00827-2 DE-627 ger DE-627 rakwb eng 610 VZ 630 640 610 VZ Masood, Rao Farhat verfasserin aut Deep Learning based Vertebral Body Segmentation with Extraction of Spinal Measurements and Disorder Disease Classification 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Assessment of medical images and diagnostic decision making of lumbar associated diseases by clinicians is invariably subjective, time consuming and challenging task. Presently, clinicians make use of either manual or semi-automated computer-aided tools to make relevant measurements for adding vote of confidence to their grading and evaluation. Lacking reliability and offering substantive dissimilarity once performed by different clinicians, these methods complicate the evaluation process. In an effort to support the decision making process of clinicians, in this paper we present a lumbar assessment framework with autonomous extraction of spinal measurements. Furthermore, an effort is made to address the challenges faced by clinicians while assessing disorders including spondylolisthesis and assessment of lumbar lordosis (LL) by proposing novel disease classification methodologies. For spondylolisthesis classification, we achieved an accuracy of 89% by using angular deviation metric whereas, 93% accuracy for determining adequacy/inadequacy in LL assessment through computation of area within enclosed lumbar curve region. Our framework involves semantic segmentation of vertebral bodies (VBs) using ResNet-UNet where we achieved DSC of 0.97 and IoU of 0.86. Subsequently, we achieved a statistically significant correlation coefficient R and encouraging mean absolute error (MAE) with clinicians’ grading for measurements involving lumbar lordotic angle (LLA), lumbosacral angle (LSA), VB dimensions and lumbar height. In addition to this, we have publicly released the dataset with all the clinicians markings at https://data.mendeley.com/datasets/k3b363f3vz/2. Assessment of medical images and diagnostic decision making of lumbar associated diseases by clinicians is invariably subjective, time consuming and challenging task. Presently, clinicians make use of either manual or semi-automated computer-aided tools to make relevant measurements for adding vote of confidence to their grading and evaluation. Lacking reliability and offering substantive dissimilarity once performed by different clinicians, these methods complicate the evaluation process. In an effort to support the decision making process of clinicians, in this paper we present a lumbar assessment framework with autonomous extraction of spinal measurements. Furthermore, an effort is made to address the challenges faced by clinicians while assessing disorders including spondylolisthesis and assessment of lumbar lordosis (LL) by proposing novel disease classification methodologies. For spondylolisthesis classification, we achieved an accuracy of 89% by using angular deviation metric whereas, 93% accuracy for determining adequacy/inadequacy in LL assessment through computation of area within enclosed lumbar curve region. Our framework involves semantic segmentation of vertebral bodies (VBs) using ResNet-UNet where we achieved DSC of 0.97 and IoU of 0.86. Subsequently, we achieved a statistically significant correlation coefficient R and encouraging mean absolute error (MAE) with clinicians’ grading for measurements involving lumbar lordotic angle (LLA), lumbosacral angle (LSA), VB dimensions and lumbar height. In addition to this, we have publicly released the dataset with all the clinicians markings at https://data.mendeley.com/datasets/k3b363f3vz/2. Lumbar spine dataset Elsevier Automated disease classification Elsevier Spinal measurements Elsevier Taj, Imtiaz Ahmad oth Khan, Muhammad Babar oth Qureshi, Muhammad Asad oth Hassan, Taimur oth Enthalten in Elsevier Yan, Yinkun ELSEVIER Independent influences of excessive body weight and elevated blood pressure from childhood on left ventricular geometric remodeling in adulthood 2017 Amsterdam [u.a.] (DE-627)ELV020088493 volume:71 year:2022 pages:0 https://doi.org/10.1016/j.bspc.2021.103230 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_60 AR 71 2022 0 |
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10.1016/j.bspc.2021.103230 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001583.pica (DE-627)ELV055775764 (ELSEVIER)S1746-8094(21)00827-2 DE-627 ger DE-627 rakwb eng 610 VZ 630 640 610 VZ Masood, Rao Farhat verfasserin aut Deep Learning based Vertebral Body Segmentation with Extraction of Spinal Measurements and Disorder Disease Classification 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Assessment of medical images and diagnostic decision making of lumbar associated diseases by clinicians is invariably subjective, time consuming and challenging task. Presently, clinicians make use of either manual or semi-automated computer-aided tools to make relevant measurements for adding vote of confidence to their grading and evaluation. Lacking reliability and offering substantive dissimilarity once performed by different clinicians, these methods complicate the evaluation process. In an effort to support the decision making process of clinicians, in this paper we present a lumbar assessment framework with autonomous extraction of spinal measurements. Furthermore, an effort is made to address the challenges faced by clinicians while assessing disorders including spondylolisthesis and assessment of lumbar lordosis (LL) by proposing novel disease classification methodologies. For spondylolisthesis classification, we achieved an accuracy of 89% by using angular deviation metric whereas, 93% accuracy for determining adequacy/inadequacy in LL assessment through computation of area within enclosed lumbar curve region. Our framework involves semantic segmentation of vertebral bodies (VBs) using ResNet-UNet where we achieved DSC of 0.97 and IoU of 0.86. Subsequently, we achieved a statistically significant correlation coefficient R and encouraging mean absolute error (MAE) with clinicians’ grading for measurements involving lumbar lordotic angle (LLA), lumbosacral angle (LSA), VB dimensions and lumbar height. In addition to this, we have publicly released the dataset with all the clinicians markings at https://data.mendeley.com/datasets/k3b363f3vz/2. Assessment of medical images and diagnostic decision making of lumbar associated diseases by clinicians is invariably subjective, time consuming and challenging task. Presently, clinicians make use of either manual or semi-automated computer-aided tools to make relevant measurements for adding vote of confidence to their grading and evaluation. Lacking reliability and offering substantive dissimilarity once performed by different clinicians, these methods complicate the evaluation process. In an effort to support the decision making process of clinicians, in this paper we present a lumbar assessment framework with autonomous extraction of spinal measurements. Furthermore, an effort is made to address the challenges faced by clinicians while assessing disorders including spondylolisthesis and assessment of lumbar lordosis (LL) by proposing novel disease classification methodologies. For spondylolisthesis classification, we achieved an accuracy of 89% by using angular deviation metric whereas, 93% accuracy for determining adequacy/inadequacy in LL assessment through computation of area within enclosed lumbar curve region. Our framework involves semantic segmentation of vertebral bodies (VBs) using ResNet-UNet where we achieved DSC of 0.97 and IoU of 0.86. Subsequently, we achieved a statistically significant correlation coefficient R and encouraging mean absolute error (MAE) with clinicians’ grading for measurements involving lumbar lordotic angle (LLA), lumbosacral angle (LSA), VB dimensions and lumbar height. In addition to this, we have publicly released the dataset with all the clinicians markings at https://data.mendeley.com/datasets/k3b363f3vz/2. Lumbar spine dataset Elsevier Automated disease classification Elsevier Spinal measurements Elsevier Taj, Imtiaz Ahmad oth Khan, Muhammad Babar oth Qureshi, Muhammad Asad oth Hassan, Taimur oth Enthalten in Elsevier Yan, Yinkun ELSEVIER Independent influences of excessive body weight and elevated blood pressure from childhood on left ventricular geometric remodeling in adulthood 2017 Amsterdam [u.a.] (DE-627)ELV020088493 volume:71 year:2022 pages:0 https://doi.org/10.1016/j.bspc.2021.103230 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_60 AR 71 2022 0 |
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10.1016/j.bspc.2021.103230 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001583.pica (DE-627)ELV055775764 (ELSEVIER)S1746-8094(21)00827-2 DE-627 ger DE-627 rakwb eng 610 VZ 630 640 610 VZ Masood, Rao Farhat verfasserin aut Deep Learning based Vertebral Body Segmentation with Extraction of Spinal Measurements and Disorder Disease Classification 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Assessment of medical images and diagnostic decision making of lumbar associated diseases by clinicians is invariably subjective, time consuming and challenging task. Presently, clinicians make use of either manual or semi-automated computer-aided tools to make relevant measurements for adding vote of confidence to their grading and evaluation. Lacking reliability and offering substantive dissimilarity once performed by different clinicians, these methods complicate the evaluation process. In an effort to support the decision making process of clinicians, in this paper we present a lumbar assessment framework with autonomous extraction of spinal measurements. Furthermore, an effort is made to address the challenges faced by clinicians while assessing disorders including spondylolisthesis and assessment of lumbar lordosis (LL) by proposing novel disease classification methodologies. For spondylolisthesis classification, we achieved an accuracy of 89% by using angular deviation metric whereas, 93% accuracy for determining adequacy/inadequacy in LL assessment through computation of area within enclosed lumbar curve region. Our framework involves semantic segmentation of vertebral bodies (VBs) using ResNet-UNet where we achieved DSC of 0.97 and IoU of 0.86. Subsequently, we achieved a statistically significant correlation coefficient R and encouraging mean absolute error (MAE) with clinicians’ grading for measurements involving lumbar lordotic angle (LLA), lumbosacral angle (LSA), VB dimensions and lumbar height. In addition to this, we have publicly released the dataset with all the clinicians markings at https://data.mendeley.com/datasets/k3b363f3vz/2. Assessment of medical images and diagnostic decision making of lumbar associated diseases by clinicians is invariably subjective, time consuming and challenging task. Presently, clinicians make use of either manual or semi-automated computer-aided tools to make relevant measurements for adding vote of confidence to their grading and evaluation. Lacking reliability and offering substantive dissimilarity once performed by different clinicians, these methods complicate the evaluation process. In an effort to support the decision making process of clinicians, in this paper we present a lumbar assessment framework with autonomous extraction of spinal measurements. Furthermore, an effort is made to address the challenges faced by clinicians while assessing disorders including spondylolisthesis and assessment of lumbar lordosis (LL) by proposing novel disease classification methodologies. For spondylolisthesis classification, we achieved an accuracy of 89% by using angular deviation metric whereas, 93% accuracy for determining adequacy/inadequacy in LL assessment through computation of area within enclosed lumbar curve region. Our framework involves semantic segmentation of vertebral bodies (VBs) using ResNet-UNet where we achieved DSC of 0.97 and IoU of 0.86. Subsequently, we achieved a statistically significant correlation coefficient R and encouraging mean absolute error (MAE) with clinicians’ grading for measurements involving lumbar lordotic angle (LLA), lumbosacral angle (LSA), VB dimensions and lumbar height. In addition to this, we have publicly released the dataset with all the clinicians markings at https://data.mendeley.com/datasets/k3b363f3vz/2. Lumbar spine dataset Elsevier Automated disease classification Elsevier Spinal measurements Elsevier Taj, Imtiaz Ahmad oth Khan, Muhammad Babar oth Qureshi, Muhammad Asad oth Hassan, Taimur oth Enthalten in Elsevier Yan, Yinkun ELSEVIER Independent influences of excessive body weight and elevated blood pressure from childhood on left ventricular geometric remodeling in adulthood 2017 Amsterdam [u.a.] (DE-627)ELV020088493 volume:71 year:2022 pages:0 https://doi.org/10.1016/j.bspc.2021.103230 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_60 AR 71 2022 0 |
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Enthalten in Independent influences of excessive body weight and elevated blood pressure from childhood on left ventricular geometric remodeling in adulthood Amsterdam [u.a.] volume:71 year:2022 pages:0 |
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Deep Learning based Vertebral Body Segmentation with Extraction of Spinal Measurements and Disorder Disease Classification |
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Assessment of medical images and diagnostic decision making of lumbar associated diseases by clinicians is invariably subjective, time consuming and challenging task. Presently, clinicians make use of either manual or semi-automated computer-aided tools to make relevant measurements for adding vote of confidence to their grading and evaluation. Lacking reliability and offering substantive dissimilarity once performed by different clinicians, these methods complicate the evaluation process. In an effort to support the decision making process of clinicians, in this paper we present a lumbar assessment framework with autonomous extraction of spinal measurements. Furthermore, an effort is made to address the challenges faced by clinicians while assessing disorders including spondylolisthesis and assessment of lumbar lordosis (LL) by proposing novel disease classification methodologies. For spondylolisthesis classification, we achieved an accuracy of 89% by using angular deviation metric whereas, 93% accuracy for determining adequacy/inadequacy in LL assessment through computation of area within enclosed lumbar curve region. Our framework involves semantic segmentation of vertebral bodies (VBs) using ResNet-UNet where we achieved DSC of 0.97 and IoU of 0.86. Subsequently, we achieved a statistically significant correlation coefficient R and encouraging mean absolute error (MAE) with clinicians’ grading for measurements involving lumbar lordotic angle (LLA), lumbosacral angle (LSA), VB dimensions and lumbar height. In addition to this, we have publicly released the dataset with all the clinicians markings at https://data.mendeley.com/datasets/k3b363f3vz/2. |
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Assessment of medical images and diagnostic decision making of lumbar associated diseases by clinicians is invariably subjective, time consuming and challenging task. Presently, clinicians make use of either manual or semi-automated computer-aided tools to make relevant measurements for adding vote of confidence to their grading and evaluation. Lacking reliability and offering substantive dissimilarity once performed by different clinicians, these methods complicate the evaluation process. In an effort to support the decision making process of clinicians, in this paper we present a lumbar assessment framework with autonomous extraction of spinal measurements. Furthermore, an effort is made to address the challenges faced by clinicians while assessing disorders including spondylolisthesis and assessment of lumbar lordosis (LL) by proposing novel disease classification methodologies. For spondylolisthesis classification, we achieved an accuracy of 89% by using angular deviation metric whereas, 93% accuracy for determining adequacy/inadequacy in LL assessment through computation of area within enclosed lumbar curve region. Our framework involves semantic segmentation of vertebral bodies (VBs) using ResNet-UNet where we achieved DSC of 0.97 and IoU of 0.86. Subsequently, we achieved a statistically significant correlation coefficient R and encouraging mean absolute error (MAE) with clinicians’ grading for measurements involving lumbar lordotic angle (LLA), lumbosacral angle (LSA), VB dimensions and lumbar height. In addition to this, we have publicly released the dataset with all the clinicians markings at https://data.mendeley.com/datasets/k3b363f3vz/2. |
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Assessment of medical images and diagnostic decision making of lumbar associated diseases by clinicians is invariably subjective, time consuming and challenging task. Presently, clinicians make use of either manual or semi-automated computer-aided tools to make relevant measurements for adding vote of confidence to their grading and evaluation. Lacking reliability and offering substantive dissimilarity once performed by different clinicians, these methods complicate the evaluation process. In an effort to support the decision making process of clinicians, in this paper we present a lumbar assessment framework with autonomous extraction of spinal measurements. Furthermore, an effort is made to address the challenges faced by clinicians while assessing disorders including spondylolisthesis and assessment of lumbar lordosis (LL) by proposing novel disease classification methodologies. For spondylolisthesis classification, we achieved an accuracy of 89% by using angular deviation metric whereas, 93% accuracy for determining adequacy/inadequacy in LL assessment through computation of area within enclosed lumbar curve region. Our framework involves semantic segmentation of vertebral bodies (VBs) using ResNet-UNet where we achieved DSC of 0.97 and IoU of 0.86. Subsequently, we achieved a statistically significant correlation coefficient R and encouraging mean absolute error (MAE) with clinicians’ grading for measurements involving lumbar lordotic angle (LLA), lumbosacral angle (LSA), VB dimensions and lumbar height. In addition to this, we have publicly released the dataset with all the clinicians markings at https://data.mendeley.com/datasets/k3b363f3vz/2. |
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