HeNeCT: Segmentation and Description of Space-occupying Lesions in Head & Neck

The HeNeCT Grand Challenge at ACM Multimedia 2025 aims to advance the segmentation and description generation of space-occupying lesions in head and neck CT scans. 

The challenge introduces two main tasks:

• Lesion Segmentation – Participants develop deep learning models to accurately segment tumors, abscesses, and cysts in contrast-enhanced head and neck CT scans.

• Lesion Description Generation – Participants develop models to generate structured, radiology-style reports for detected lesions, capturing lesion type, size, shape, and other critical features.

The dataset for the challenge will be annotated by expert radiologists and will include videos with tumor, abscess, and cyst cases. The data will be released in three stages, progressively increasing in complexity. Evaluation metrics for segmentation include IoU, DSC, and HD95, while evaluation for description generation includes BLEU, ROUGE, METEOR, and CIDEr.

• Thanh Dinh Le (Thong Nhat Hospital and University of Health Science, VNU-HCM, Vietnam)

• Thao Thi Phuong Dao (Thong Nhat Hospital and University of Science, VNU-HCM, Vietnam)

• Trong-Le Do (University of Science, VNU-HCM, Vietnam)

• Mai-Khiem Tran (University of Science, VNU-HCM, Vietnam)

• Trung-Nghia Le (University of Science, VNU-HCM, Vietnam)

• Cathal Gurrin (Dublin City University, Ireland)

• Minh-Triet Tran (University of Science, VNU-HCM, Vietnam)

• TBA