Datasets and challenges summary of the head and neck¶
Datasets and challenges summary of the basic brain image analysis.¶
Most of these datasets and challenges are the segmentation tasks, while some of the other tasks are about reconstruction. WM is short for white matter; GM is short for graymatter; CSF is short for cerebrospinal fluid.
Index |
Name |
Year |
Modality |
Segmentation/Focus |
---|---|---|---|---|
1 |
2019 |
T1w |
WM, GM, CSF, etc. |
|
2 |
2019 |
T1, T2 |
WM, GM, CSF, etc. |
|
3 |
2017 |
T1w, T2w |
WM, GM, CSF, etc. |
|
4 |
2018 |
T1w, T1w-IR, T2-FLAIR |
WM, GM, CSF, etc. |
|
5 |
2015 |
T1w, T1WIR, T2FLAIR |
WM, GM, CSF, etc. |
|
6 |
2013 |
T1w, T1W-IR, T2-FLAIR |
WM, GM, CSF, etc. |
|
7 |
2012 |
T1, T2 |
WM, GM, CSF, etc. |
|
8 |
2017 |
T1 |
WM, GM, CSF, etc. |
|
9 |
2017 |
Cerebellum |
||
10 |
2012 |
T1w, etc. |
brain atlases |
|
11 |
2012 |
T1, etc. |
brain atlases |
|
12 |
2007 |
T1 |
Caudate |
|
13 |
2020 |
CT |
Cranioplasty |
|
14 |
2020 |
T1, T2 |
the non-linear mapping between different resolutions |
|
15 |
2020 |
dMRI |
white matter reconstruction |
|
16 |
2020 |
MRI |
brain image reconstruction |
|
17 |
2020 |
T1w |
MRI reconstruction |
|
18 |
2018 |
T1w, T2w, etc. |
Accelerating magnetic resonance imaging |
|
19 |
2018 |
DW |
DW MRI registration and enhancement |
|
20 |
2013 |
Diffusion |
Diffusion MRI reconstruction |
|
21 |
2012 |
Diffusion |
Diffusion MRI reconstruction |
|
22 |
2019 |
MRI, Ultrasound |
image registration |
|
23 |
2018 |
MRI, Ultrasound |
image registration |
Datasets and challenges summary of the brain lesion and tumor segmentation task.¶
The focuses on the datasets and challenges include the brain tumor, particularly glioma, and the lesions about the stroke.
Index |
Name |
Year |
Modality |
Focus |
---|---|---|---|---|
27 |
2020 |
MRI |
cerebral aneurysm segmentation |
|
28 |
2020 |
MRI |
cerebral aneurysm rupture risk estimation |
|
29 |
2020 |
MRI |
cerebral aneurysm detection |
|
30 |
2020 |
T1, T1Gd, T2, T2-FLAIR |
Multi-model & brain tumor |
|
31 |
2019 |
T1, T1Gd, T2, T2-FLAIR |
Multi-model & brain tumor |
|
32 |
2018 |
T1, T1Gd, T2, T2-FLAIR |
Multi-model & brain tumor |
|
33 |
2017 |
T1, T1Gd, T2, T2-FLAIR |
Multi-model & brain tumor |
|
34 |
2016 |
T1, T1c, T2, T2w-FLAIR |
Multi-model & brain tumor |
|
35 |
2015 |
T1, T1c, T2, T2w-FLAIR |
Multi-model & brain tumor |
|
36 |
2014 |
T1, T1c, T2, T2w-FLAIR |
Multi-model & brain tumor |
|
37 |
2013 |
T1, T1c, T2, T2w-FLAIR |
Multi-model & brain tumor |
|
38 |
2012 |
T1, T1c, T2, T2w-FLAIR |
Multi-model & brain tumor |
|
39 |
2018 |
DWI, Perfusion, etc. |
ischemic stroke lesion |
|
40 |
2017 |
T2w, FLAIR, etc. |
ischemic stroke lesion |
|
41 |
2016 |
ADC, Perfusion, etc. |
ischemic stroke lesion |
|
42 |
2015 |
T1, T2, FLAIR, etc. |
ischemic stroke lesion |
|
43 |
Computed Tomography Images for Intracranial Hemorrhage Detection and Segmentation |
2020 |
CT |
intracranial hemorrhage detection and segmentation |
44 |
2019 |
CT |
intracranial hemorrhage detection |
|
46 |
2018 |
T1w, FLAIR, T2w, etc. |
brain tumor |
|
47 |
2017 |
T1w, T2w |
Low-grade gliomas |
|
48 |
2017 |
T1w |
anatomical segmentation of brain after stroke |
|
49 |
2016 |
T1w, DP/T2, FLAIR |
multiple sclerosis |
|
50 |
Longitudinal Multiple Sclerosis Lesion Segmentation Challenge (2015) |
2015 |
T1w, T2w, PDw, FLAIR |
longitudinal multiple sclerosis lesion |
51 |
2008 |
T1, T2, FLAIR |
multiple sclerosis lesion |
Datasets and challenges summary of brain disease classification tasks.¶
AD is short for Alzheimer’s detection; PD is short for Parkinson’s disease; SCA2 is short for Spinocerebellar ataxia type II; NC is short for normal control.
Index |
Name |
Year |
Modality |
Disease |
Category |
---|---|---|---|---|---|
52 |
2004 |
MRI, PET |
AD |
NC, MCI, AD |
|
53 |
2009 |
MRI, PET |
AD |
NC, MCI, AD |
|
54 |
2011 |
MRI, PET |
AD |
NC, EMCI, LMCI, AD |
|
55 |
2016 |
MRI, PET |
AD |
NC, EMCI, LMCI, AD |
|
56 |
2007 |
T1w |
AD |
NC, AD |
|
57 |
2009 |
T1w |
AD |
NC, AD |
|
58 |
2019 |
T1w, T2w, FLAIR, PET, etc. |
AD |
NC, AD |
|
59 |
2019 |
AD |
NC, AD |
||
60 |
2017 |
MRI,PET,DTI,CSF, etc. |
AD |
NC, MCI, AD |
|
61 |
2017 |
T1w |
AD |
NC, AD |
|
62 |
2014 |
T1w |
AD |
NC, MCI, AD |
|
63 |
2019 |
T1, event, bold |
PD |
NC, PD |
|
64 |
2018 |
T1w, bold |
PD |
NC, PD |
|
65 |
2018 |
T1w, dwi |
SCA2 |
NC, SCA2 |
|
66 |
2016 |
T1w, DWI, etc. |
mild traumatic brain injury outcome prediction |
healthy, patient category I or patient category II |
Datasets and challenges summary of eye-disease-concerned tasks.¶
Tasks include classification (C), segmentation (S), location (L), and tool annotation (1).
Index |
Name |
Year |
Modality |
Disease |
Task |
|||
---|---|---|---|---|---|---|---|---|
67 |
Retinal Image Analysis for multi-Disease Detection Challenge |
2020 |
fundus photo |
fundus-concered diseases |
C |
|||
68 |
The 2nd Diabetic Retinopathy – Grading and Image Quality Estimation Challenge |
2020 |
fundus photo |
diabetic retinopathy |
C |
|||
69 |
2020 |
fundus photo |
glaucoma |
C |
S |
L |
||
70 |
2018 |
fundus photo |
glaucoma |
C |
S |
L |
||
71 |
2019 |
OCT |
closure glaucoma |
C |
L |
|||
72 |
2019 |
fundus photo |
(vessel extraction) |
S |
||||
73 |
2019 |
fundus photo |
diabetes, glaucoma, cataract, AMD, hypertension, myopia, and others |
C |
||||
74 |
2019 |
fundus photo |
Pathologic Myopia |
C |
S |
L |
||
75 |
2019 |
fundus photo |
diabetic retinopathy |
C |
||||
76 |
2018 |
fundus photo |
Age-related Macular degeneration |
C |
L |
|||
77 |
2018 |
fundus photo |
diabetic retinopathy and diabetic macular edema |
C |
S |
L |
||
78 |
2018 |
OCT |
macular degeneration and diabetic retinopathy |
C |
||||
79 |
2017 |
OCT |
diabetic retinopathy |
C |
||||
80 |
2017 |
video |
(automatic tool annotation) |
1 |
||||
81 |
2017 |
OCT |
(fluid segmentation) |
S |
||||
82 |
2015 |
fundus photo |
diabetic retinopathy |
C |
||||
83 |
2015 |
OCT |
diabetic macular edema |
S |
||||
84 |
2009 |
fundus photo |
diabetic retinopathy |
L |
Datasets and challenges summary of other subjects in head and neck.¶
Tasks includes classification (C), segmentation (S), detection (D), and location (L).
Index |
Name |
Year |
Modality |
Focus |
Task |
---|---|---|---|---|---|
85 |
2020 |
CT, PET |
segmentation of head and neck primary tumors |
S |
|
86 |
Thyroid Nodule Segmentation and Classification in Ultrasound Images |
2020 |
Ultrasound |
thyroid gland nodules diagnosis |
D |
87 |
2019 |
CT |
head and neck squamous cell carcinoma |
S |
|
88 |
2019 |
T2w |
Soft tissue and tumor |
S |
|
89 |
2017 |
PET, CT |
tumor |
C |
|
90 |
2015 |
CT |
tumor |
S |
|
91 |
2016 |
Ultrasound |
nerve segmentation |
S |
|
92 |
Grand Challenges in Dental X-ray Image Analysis: Challenge 1 |
2015 |
X-Ray |
Cephalometric X-Ray image location |
L |
93 |
Grand Challenges in Dental X-ray Image Analysis: Challenge 2 |
2015 |
X-Ray |
detection and segmentation of caries |
S |
94 |
2010 |
MRI |
parotid gland |
S |
|
95 |
2009 |
CT |
multi organs and tissues |
S |
|
96 |
2009 |
CTA |
carotid bifurcation |
S |
Datasets summary of behavioral and perception concerning tasks.¶
Index |
Name |
Year |
Modality |
Task |
Stimulation |
---|---|---|---|---|---|
97 |
Cognitive control of sensory pain encoding in the pregenual anterior cingulate cortex. |
2020 |
T1w, bold |
Sensory pain encoding |
pain |
98 |
2020 |
T1w, bold, events |
Different between expert and novices in cortical representations of source code |
brain action |
|
99 |
Reconstructing Faces from fMRI Patterns using Deep Generative Neural Networks. |
2019 |
T1w |
Faces Reconstruction |
vision |
100 |
2019 |
T1, bold |
effect of iTBS on fronto-striatal network;, ROI segmentation |
iTBS |
|
101 |
2018 |
T1w, inplaneT2, bold, events |
Image reconstruction from human brain activity |
vision |
|
102 |
2018 |
bold, T2w, T1w, dwi, fieldmap |
Brain reaction of vision |
vision |
|
103 |
Neural Processing of Emotional Musical and Nonmusical Stimuli in Depression |
2018 |
T1w, bold |
Brain reaction of audition |
audition |
104 |
2018 |
inplaneT2, bold, T1w |
Image reconstruction from human brain activity |
vision |
|
105 |
2018 |
T1w, inplaneT2, bold, events |
Image reconstruction from human brain activity |
vision |
|
106 |
Adjudicating between face-coding models with individual-face fMRI responses |
2018 |
T1w etc. |
Decoding face from brain activity |
vision |
107 |
T1-weighted structural MRI study of cannabis users at baseline and 3 years follow up |
2018 |
T1w |
Cannabis impact of brain |
cannabis |