Improved U-Net Based Cervical Exfoliated Cell Image Segmentation

This paper presents a framework specifically designed for the task of segmenting microscopic images of cervical cancer cells. Cervical cancer, as the second most common female malignancy globally, relies on early detection to boost survival and quality of life. Traditional manual detection of such images is inefficient, subjective, and prone to misdiagnosis or missed diagnosis, delaying treatment and skewing plans. Thus, accurately segmenting cervical cancer cell microscopic images is crucial for early diagnosis and patient care. Precise segmentation clarifies structures like nuclei and cytoplasm, helping doctors spot subtle changes in cell morphology, size, and texture to identify abnormal cells (e.g., keratinized or vacuolated ones), aiding early diagnosis. It enables timely lesion assessment, personalized treatment (surgery, chemo/radiotherapy, immunotherapy), and real-time monitoring to adjust strategies. The proposed IDFU-Net with Markov random field post-processing enhances segmentation accuracy, promising an objective, efficient tool for clinicians to advance early cervical cancer management and improve prognosis.