-
Notifications
You must be signed in to change notification settings - Fork 0
/
segmentation.html
273 lines (247 loc) · 16.6 KB
/
segmentation.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
<!DOCTYPE HTML>
<html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>Robust Segmentation</title>
<meta name="author" content="Nicola K Dinsdale">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" type="text/css" href="stylesheet.css">
</head>
<body>
<table style="width:100%;max-width:800px;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr style="padding:0px">
<td style="padding:0px">
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;">
<tbody>
<tr style="padding:0px">
<td style="padding:2.5%;width:63%;vertical-align:middle">
<p style="text-align:center">
<name>Robust Segmentation</name>
</p>
<p>
The delineation of regions in medical images - segmentation - is a key task across modalities and organs. Methods developed need to produce segmentations that are biologically meaningful across scanners and imaging protocols.
</p>
<p style="text-align:center">
<a href="index.html">Home</a>  / 
<a
href="harmonisation.html">Harmonisation</a>
 / 
<a href="segmentation.html">Segmentation</a>  / 
<a href="translation.html">Translation</a>  / 
<a href="privacy.html">Privacy</a>  / 
<a href="explainable.html">Explainable AI</a>
</p>
</td>
<td style="padding:2.5%;width:40%;max-width:40%">
<a href="images/seg.png"><img style="width:100%;max-width:100%" alt="profile photo" src="images/seg.png" class="hoverZoomLink"></a>
</td>
</tr>
</tbody>
</table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;">
<tbody>
<tr>
<td style="padding:20px;width:100%;vertical-align:middle">
<p style="text-align:center">
<heading>Papers</heading>
</p>
</td>
</tr>
</tbody>
</table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;">
<tbody>
<tr>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/maddy.png'
width="160"
height="150">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://www.sciencedirect.com/science/article/pii/S1361841524001476">
<papertitle>Anatomically plausible segmentations: Explicitly preserving topology through prior deformations </papertitle>
</a>
<br>
<a href="https://scholar.google.com/citations?user=58_isAMAAAAJ&hl=en">Madeleine K Wyburd</a>, <strong> Nicola K Dinsdale </strong>, <a href="https://www.pmb.ox.ac.uk/person/dr-ana-namburete">Ana IL Namburete</a>, <a href="https://www.ndcn.ox.ac.uk/team/mark-jenkinson">Mark Jenkinson</a>
<br>
<em> Medical Image Analysis 2024 </em>
<br>
<a href="https://www.sciencedirect.com/science/article/pii/S1361841524001476">Paper</a> / <a href="https://github.com/mwyburd/TEDS-Net">Code</a>
<p></p>
<p> Our model, TEDS-Net, generates anatomically plausible segmentations through deforming a prior shape with the same topology as the anatomy of interest. </p>
</td>
</tr>
<tr>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/unifed.png' width="160" height="150">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://www.biorxiv.org/content/10.1101/2024.02.05.578912v1.full.pdf">
<papertitle>UniFed: A unified deep learning framework for
segmentation of partially labelled, distributed
neuroimaging data </papertitle>
</a>
<br>
<strong> Nicola K Dinsdale</strong>, <a
href="https://www.ndcn.ox.ac.uk/team/mark-jenkinson">Mark Jenkinson</a>, <a
href="https://www.pmb.ox.ac.uk/person/dr-ana-namburete">Ana IL Namburete</a>
<br>
<em>bioRxiv</em>, 2024
<br>
<a href="https://nkdinsdale.github.io/unifed_proj/">Project Page</a> / <a
href="https://www.biorxiv.org/content/10.1101/2024.02.05.578912v1.full.pdf">Paper</a> / <a
href="https://github.com/nkdinsdale/UniFed">Code</a>
<p></p>
<p> We, therefore, propose UniFed, a unified federated harmoni-
sation framework, which enables three key processes to be completed: 1) the training of a federated partially labelled
harmonisation network, 2) the selection of the most appropriate pretrained model for a new unseen
site, and 3) the incorporation of a new site into the harmonised federation. </p>
</td>
</tr>
<tr>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/pruning.png'
width="160"
height="150">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://www.sciencedirect.com/science/article/pii/S1361841522002213">
<papertitle>STAMP: Simultaneous Training and Model Pruning for Low Data Regimes in Medical Image Segmentation</papertitle>
</a>
<br>
<strong> Nicola K Dinsdale </strong>, <a href="https://www.ndcn.ox.ac.uk/team/mark-jenkinson">Mark Jenkinson</a>, <a href="https://www.pmb.ox.ac.uk/person/dr-ana-namburete">Ana IL Namburete</a>
<br>
<em>Medical Image Analysis</em>, 2022
<br>
<a href="https://nkdinsdale.github.io/pruning_proj/">Project Page</a> / <a href="https://www.sciencedirect.com/science/article/pii/S1361841522002213">Paper</a> / <a href="https://github.com/nkdinsdale/STAMP">Code</a>
<p></p>
<p>Development of an algorithm to enable simultaneous training and pruning, enabling segmentation in low data domains. Also introduces Targeted Dropout to stabilise the pruning.</p>
</td>
</tr>
<tr>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/wmh.png'
width="160"
height="150">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://ieeexplore.ieee.org/abstract/document/9761539">
<papertitle>Omni-Supervised Domain Adversarial Training for White Matter Hyperintensity Segmentation in the UK Biobank</papertitle></a> <br>
<a href="https://www.ndcn.ox.ac.uk/team/vaanathi-sundaresan">Vaanathi Sundaresan,</a> <strong> Nicola K Dinsdale</strong>, Ludovica Griffanti, <a href="https://www.ndcn.ox.ac.uk/team/mark-jenkinson">Mark Jenkinson</a>,
<br>
<em>ISBI 2022 </em> <span style="color:red;">[Oral Presentation]</span>
<br>
<a href="https://nkdinsdale.github.io/isbi_proj/">Project Page</a> / <a href="https://ieeexplore.ieee.org/abstract/document/9761539">Paper</a> / <a href="https://github.com/v-sundaresan/omnisup_agepred_semidann">Code</a>
<p></p>
<p> Exploring the use of omni-supervised learning for white matter hyperintensity segmentation, using age prediction as the selection criteria. Leads to significant increase in sensitivity especially for small lesions. </p>
</td>
</tr>
<tr>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/hippocampus.png'
width="160"
height="150">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://onlinelibrary.wiley.com/doi/10.1002/hbm.25858?af=R">
<papertitle>The impact of transfer learning on 3D deep learning convolutional neural network segmentation of the hippocampus in mild cognitive impairment and Alzheimer disease subjects</papertitle>
</a>
<br>
Erica Balboni, Luca Nocetti, Chiara Carbone, <strong> Nicola K Dinsdale </strong>, Maurilio Genovese, Gabriele Guidi, Marcella Malagoli, Annalisa Chiari, <a href="https://www.pmb.ox.ac.uk/person/dr-ana-namburete">Ana IL Namburete</a>, <a href="https://www.ndcn.ox.ac.uk/team/mark-jenkinson">Mark Jenkinson</a>, Giovanna Zamboni,
<br>
<em>Human Brain Mapping 2022</em>
<br>
<a href="https://onlinelibrary.wiley.com/doi/10.1002/hbm.25858?af=R">Paper</a> / <a href="https://github.com/ericabalboni/SWANS">Code</a>
<p></p>
<p>Exploration of the use of my SWANS algorithm for clinical data, across disease groups - SWANS first presented at <a href="https://link.springer.com/chapter/10.1007/978-3-030-32248-9_32">MICCAI 2019</a> </p>
</td>
</tr>
<tr>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/maddy.png'
width="160"
height="150">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://mwyburd.github.io/TEDS-NET_wp/">
<papertitle> TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations </papertitle>
</a>
<br>
<a href="https://scholar.google.com/citations?user=58_isAMAAAAJ&hl=en">Madeleine K Wyburd</a>, <strong> Nicola K Dinsdale </strong>, <a href="https://www.pmb.ox.ac.uk/person/dr-ana-namburete">Ana IL Namburete</a>, <a href="https://www.ndcn.ox.ac.uk/team/mark-jenkinson">Mark Jenkinson</a>
<br>
<em>MICCAI</em>, 2021 <span style="color:red;">[Early Acceptance]</span>
<br>
<a href="https://mwyburd.github.io/TEDS-NET_wp/">Project Page</a> / <a href="https://arxiv.org/abs/2107.13542">Paper</a> / <a href="https://github.com/mwyburd/TEDS-Net">Code</a>
<p></p>
<p> Novel segmentation method guaranteeing accurate topology.</p>
</p>
</tr>
<tr>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/wmh.png'
width="160"
height="150">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://doi.org/10.1016/j.media.2021.102215">
<papertitle> Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images </papertitle>
</a>
<br>
<a href="https://www.ndcn.ox.ac.uk/team/vaanathi-sundaresan">Vaanathi Sundaresan</a>, Giovanna Zamboni, <strong> Nicola K Dinsdale </strong>, Peter Rothwell, Ludovica Griffanti, <a href="https://www.ndcn.ox.ac.uk/team/mark-jenkinson">Mark Jenkinson</a>
<br>
<em> Medical Image Analysis </em> 2021
<br>
<a href="https://doi.org/10.1016/j.media.2021.102215">Paper</a> / <a href="https://git.fmrib.ox.ac.uk/vaanathi/true_net_wmh_segmentation_pytorch">Code</a>
<p></p>
<p> Comparison of DA methods for white matter hyperintensity segmentation, comparing methods including my proposed method: <a href="https://nkdinsdale.github.io/harmproj/">paper</a>.</p>
</td>
</tr>
<tr>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/cardiac.png'
width="160"
height="150">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://link.springer.com/chapter/10.1007/978-3-030-68107-4_20">
<papertitle> A 2-step deep learning method with domain adaptation for multi-centre, multi-vendor and multi-disease cardiac magnetic resonance segmentation </papertitle>
</a>
<br>
Jorge Corral Acero, <a href="https://www.ndcn.ox.ac.uk/team/vaanathi-sundaresan">Vaanathi Sundaresan</a>, <strong> Nicola K Dinsdale </strong>, Vicente Grau, <a href="https://www.ndcn.ox.ac.uk/team/mark-jenkinson">Mark Jenkinson</a>
<br>
<em> STACOM 2020 - 6th place </em>
<br>
<a href="https://link.springer.com/chapter/10.1007/978-3-030-68107-4_20">Paper</a>
<p></p>
<p> Segmentation of cardiac MRI across sites, using the method proposed in my <a href="https://nkdinsdale.github.io/harmproj/">paper</a></p>
</td>
</tr>
<tr>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/hippocampus.png'
width="160"
height="150">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://link.springer.com/chapter/10.1007/978-3-030-32248-9_32">
<papertitle> Spatial warping network for 3D segmentation of the hippocampus in MR images </papertitle>
</a>
<br>
<strong> Nicola K Dinsdale </strong>, <a href="https://www.ndcn.ox.ac.uk/team/mark-jenkinson">Mark Jenkinson</a>, <a href="https://www.pmb.ox.ac.uk/person/dr-ana-namburete">Ana IL Namburete</a>
<br>
<em>MICCAI</em>, 2019 <span style="color:red;">[Early Acceptance]</span>
<br>
<a href="https://link.springer.com/chapter/10.1007/978-3-030-32248-9_32">Paper</a> / <a href="https://github.com/nkdinsdale/SWANS">Code</a>
<p></p>
<p> Novel segmentation method based on a spatial transformer network.</p>
</td>
</tr>
</tbody>
</table>
<br>
<p style="text-align:right;font-size:small;">
The template of this webpage is from <a href="https://github.com/jonbarron/jonbarron_website">source code</a>.
</p>
</td>
</tr>
</table>
</body>
</html>