Analyzing the Impact of Photometric Data Augmentation on Medical Instance Segmentation Performance

Öz

This study analyzes the effect of photometric data augmentation strategies on medical instance segmentation using the Kvasir-SEG dataset. Mask R-CNN X101-FPN and YOLOv8l-seg are adopted as representative two-stage and one-stage segmentation models. Although data augmentation is commonly employed to enhance robustness in image classification, its systematic evaluation in instance segmentation remains limited due to the need to preserve pixel-level mask integrity during transformations. In this work, eight photometric augmentation techniques, including Hue, Saturation, Grayscale, Brightness, Contrast, Noise, Blur and Cutout, are applied both individually and through structured multi-level combinations. Each augmentation is evaluated within single, double, triple, and full pipelines. Segmentation performance is measured using mean Average Precision (mAP) based on the COCO evaluation protocol. The experimental results show that color-based augmentations provide more reliable accuracy improvements than distortion-based methods in polyp segmentation tasks, while excessive augmentation depth may slow convergence and restrict performance gains. This study presents a systematic analysis of augmentation depth and diversity and offers practical guidance for designing effective augmentation pipelines in medical instance segmentation.

Anahtar Kelimeler

• Deep Learning
• computer vision
• data augmentation
• medical instance segmentation

Tıbbi Örnek Segmentasyonunda Fotometrik Veri Artırmanın Performansa Etkisinin Analizi

Öz

Bu çalışma, Kvasir-SEG veri seti kullanılarak fotometrik veri artırma stratejilerinin tıbbi örnek segmentasyonu üzerindeki etkisini incelemektedir. Mask R-CNN X101-FPN ve YOLOv8l-seg modelleri, iki aşamalı ve tek aşamalı örnek segmentasyon yaklaşımlarını temsil etmektedir. Veri artırma, görüntü sınıflandırmada yaygın olarak kullanılmasına rağmen, piksel düzeyinde maske bütünlüğünün korunması gerekliliği nedeniyle örnek segmentasyonu alanındaki sistematik çalışmalar sınırlıdır. Bu çalışmada Hue, Saturation, Grayscale, Brightness, Contrast, Noise, Blur ve Cutout olmak üzere sekiz fotometrik veri artırma yöntemi, tekli ve çok seviyeli kombinasyonlar halinde uygulanmıştır. Performans, COCO değerlendirme protokolüne göre hesaplanan ortalama doğruluk (mAP) metriği ile değerlendirilmiştir. Sonuçlar, renk tabanlı artırmaların polip segmentasyonu görevlerinde bozulma tabanlı yöntemlere kıyasla daha tutarlı doğruluk artışları sağladığını, aşırı artırma derinliğinin ise yakınsamayı yavaşlatabildiğini göstermektedir. Bu çalışma, tıbbi örnek segmentasyonu için etkili veri artırma stratejilerine yönelik sistematik bir değerlendirme sunmaktadır.

Anahtar Kelimeler

• Derin Öğrenme
• Bilgisayarlı Görü
• Veri Artırma
• Tıbbi Örnek Segmentasyonu

Kaynakça

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