Datasets:

Angelou0516
/

HNTSMRG

patient_id int32 2 201	timepoint stringclasses 2 values	num_slices int32 57 168	selected_slice int32 17 120
2	preRT	83	47
2	midRT	76	41
3	preRT	77	35
3	midRT	60	23
4	preRT	74	36
4	midRT	77	38
5	preRT	81	42
5	midRT	81	41
6	preRT	75	52
6	midRT	76	52
8	preRT	65	37
8	midRT	67	40
10	preRT	75	44
10	midRT	76	40
11	preRT	110	49
11	midRT	114	53
12	preRT	80	38
12	midRT	80	41
13	preRT	70	36
13	midRT	67	34
14	preRT	80	49
14	midRT	83	50
17	preRT	119	87
17	midRT	121	40
18	preRT	74	29
18	midRT	71	26
20	preRT	72	31
20	midRT	75	32
21	preRT	133	55
21	midRT	134	67
22	preRT	67	29
22	midRT	66	28
23	preRT	68	45
23	midRT	68	45
24	preRT	68	30
24	midRT	68	41
25	preRT	65	36
25	midRT	63	31
26	preRT	83	62
26	midRT	82	48
27	preRT	61	37
27	midRT	60	37
29	preRT	75	57
29	midRT	152	76
30	preRT	65	36
30	midRT	70	35
31	preRT	125	74
31	midRT	128	65
32	preRT	111	62
32	midRT	113	62
33	preRT	72	49
33	midRT	75	35
34	preRT	113	37
34	midRT	125	40
36	preRT	65	39
36	midRT	65	35
37	preRT	82	48
37	midRT	85	42
39	preRT	122	56
39	midRT	123	60
41	preRT	73	53
41	midRT	71	54
42	preRT	75	38
42	midRT	77	38
44	preRT	80	43
44	midRT	76	39
45	preRT	67	42
45	midRT	63	37
46	preRT	74	25
46	midRT	76	25
47	preRT	84	57
47	midRT	168	78
48	preRT	66	39
48	midRT	67	36
49	preRT	62	25
49	midRT	65	31
50	preRT	70	38
50	midRT	68	38
52	preRT	69	35
52	midRT	113	55
53	preRT	71	27
53	midRT	145	65
55	preRT	79	59
55	midRT	81	58
56	preRT	74	50
56	midRT	80	40
57	preRT	132	78
57	midRT	141	63
58	preRT	74	36
58	midRT	73	35
60	preRT	86	33
60	midRT	80	37
61	preRT	74	32
61	midRT	74	34
63	preRT	73	35
63	midRT	70	37
64	preRT	72	34
64	midRT	124	51
66	preRT	80	34
66	midRT	78	35

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HNTSMRG 2024 — Head and Neck Tumor Segmentation for MR-Guided Applications

Public training data of the HNTS-MRG 2024 MICCAI Challenge: T2-weighted MRI of head-and-neck cancer patients with expert-consensus segmentations of the primary gross tumor volume (GTVp) and metastatic involved lymph nodes (GTVn), captured at two timepoints — pre-radiotherapy (pre-RT) and mid-radiotherapy (mid-RT).

Dataset Summary

Field	Details
Modality	T2-weighted anatomical MRI (mix of fat-sup and non-fat-sup)
Body Part	Head and neck (oropharyngeal cancer, cancer of unknown primary)
Subjects	150 (training, public)
Timepoints / patient	2 (pre-RT + mid-RT)
Labels	0 = background, 1 = GTVp (primary tumor), 2 = GTVn (involved lymph nodes)
Annotation	3–4 expert physicians per case, fused via STAPLE consensus (faculty arbitration in extreme-disagreement cases)
Total Size	~15 GB
License	CC-BY-NC-4.0
Source	Zenodo: https://zenodo.org/records/11199559

Tasks

The HNTSMRG 2024 Challenge defines two segmentation tasks, both supported by this single training set:

Task 1 — pre-RT segmentation (GTVp + GTVn on baseline MRI)
Task 2 — mid-RT segmentation (GTVp + GTVn on intra-treatment MRI; pre-RT data registered to mid-RT space is provided as a prior)

Data Structure

HNTSMRG24_train/
└── {patient_id}/
    ├── preRT/
    │   ├── {patient_id}_preRT_T2.nii.gz             # pre-RT T2w MRI
    │   └── {patient_id}_preRT_mask.nii.gz           # pre-RT GT mask (STAPLE consensus)
    └── midRT/
        ├── {patient_id}_midRT_T2.nii.gz             # mid-RT T2w MRI
        ├── {patient_id}_midRT_mask.nii.gz           # mid-RT GT mask (STAPLE consensus)
        ├── {patient_id}_preRT_T2_registered.nii.gz  # pre-RT T2 registered to mid-RT space
        └── {patient_id}_preRT_mask_registered.nii.gz # pre-RT mask registered to mid-RT space

Pre-RT-to-mid-RT registration was performed with SimpleITK (rigid + deformable) by the challenge organizers and is intended as an optional prior for Task 2.

Citation

@inproceedings{wahid2025hntsmrg,
  title     = {Overview of the Head and Neck Tumor Segmentation for Magnetic Resonance Guided Applications (HNTS-MRG) 2024 Challenge},
  author    = {Wahid, Kareem A. and Dede, Cem and El-Habashy, Dina M. and Kamel, Serageldin and Rooney, Michael K. and Khamis, Yomna and others and Fuller, Clifton D.},
  booktitle = {Head Neck Tumor Segmentation for MR-Guided Applications},
  series    = {Lecture Notes in Computer Science},
  volume    = {15273},
  pages     = {1--35},
  year      = {2025},
  doi       = {10.1007/978-3-031-83274-1_1}
}

@dataset{wahid2024hntsmrgzenodo,
  title     = {Training Dataset for HNTSMRG 2024 Challenge},
  author    = {Wahid, Kareem and Dede, Cem and Naser, Mohamed and Fuller, Clifton},
  year      = {2024},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.11199559},
  url       = {https://doi.org/10.5281/zenodo.11199559}
}

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