Deep Image Prior (DIP) has proven to be a strong unsupervised method that utilizes the inductive bias of convolutional neural networks to address a range of inverse image tasks without the need for large-scale labeled datasets. Although DIP has been effectively used for denoising, super-resolution, and inpainting, its application to image segmentation is still quite underdeveloped. Here, we introduce in this work a thorough review of DIP, traditional segmentation techniques, and some of the latest developments in DIP-based segmentation. We present a new architectural boost, a deeper U-Net dedicated to DIP-based segmentation. With the extension of the encoder-decoder architecture and the retention of symmetric skip connections, the network’s capacity for encoding intricate spatial priors and semantic consistency from a single image instance is greatly enhanced. Quantitative evaluations on varied datasets, natural (BSD68), biomedical (Kvasir-SEG), and synthetic segmentation tasks validate consistent performance gains in both denoising metrics (PSNR, SSIM) and segmentation metrics (Dice score, IoU) relative to baseline U-Net under DIP optimization. Qualitative outcomes describe sharper edges, improved noise resistance, and superior structural alignment. Our results demonstrate the interaction between increased depth and DIP’s regularization inherent in the architecture, supporting the notion that model depth can be used as a proxy for learned priors in label-free segmentation. This paper not only advances the use of DIP for segmentation but also offers a simple and effective architectural template for subsequent research in unsupervised image analysis.