Optimizing Channel Selection for U-Net Segmentation in MIBI-TOF of Triple-Negative Breast Cancer - Sousa, Victor Gabriel Pereira de; Luz, Daniel de Sousa; Sousa, Daniel Rodrigues de; Araújo, Flávio Henrique Duarte de (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2026: Anais do XXVI Simpósio Brasileiro de Computação Aplicada à SaúdeDO - 10.5753/sbcas.2026.21491, 2026)
Multiplexed ion-beam imaging enables simultaneous quantification of dozens of protein markers at subcellular resolution. In this paper, we investigate how channel selection impacts U-Net–based segmentation performance on a public triple-negative breast cancer MIBI-TOF dataset with 41 images each with 48 channels. Our experiments show that compact, data-driven marker subsets achieve higher Dice than the recommended panel, suggested by the dataset paper, while using fewer channels. In summary, our experiments show that it is possible to reduce from seven channels to one without losing segmentation accuracy: our best single-channel model (dsDNA) achieves 89.36% Dice, practically matching the 89.41% Dice obtained by the full recommended panel.