Cancer Detection and Location Methods in Histopathological Images

Yıl 2021, Sayı: 23, 608 - 616, 30.04.2021

Zehra Bozdağ , Muhammed Fatih Talu

https://doi.org/10.31590/ejosat.888836

Cited By: 1

Öz

Tumor detection in the histopathological images of breast lymph nodes is one of the most important findings in the diagnosis of breast cancer. Histopathological images are carefully examined by pathologists and tumor detection is performed. This process causes both workload density and a subjective assessment. The CAMELYON16 dataset has been used by the International Symposium on Biomedical Image (ISBI) for automatic detection of the tumor on histopathological images. Data sets created at different level resolutions of whole slide images (WSI) have been analyzed using Faster RCNN. The data sets with different image sizes created from 3rd resolution levels of WSI have been analyzed using Mask RCNN. Finally, the HMS & MIT method, which ranked in ISBI, has been applied on a limited data set, and its performance on the histopathological image data sets has been compared with the Faster RCNN and Mask RCNN algorithms. Although Mask RCNN (57% AUC) has been trained with low-resolution level images (3rd levels) has an accuracy value close to the HMS & MIT (58% AUC) (using high-resolution level images, 0th level) method. Also, for a WSI analysis, the result has been obtained with Faster RCNN as soon as possible (0.58 hours)

Anahtar Kelimeler

Semantic Segmentation, Deep Learning, Faster RCNN, Medical Image Processing, Mask RCNN, Object Detection, Tumor Detection

Histopatolojik Görüntülerde Kanser Tespit ve Lokasyon Yöntemleri

Öz

Meme lenf düğümlerinin histopatolojik görüntülerinde tümör tespiti meme kanseri teşhisinde en önemli bulgulardan bir tanesidir. Histopatolojik görüntüler, patologlar tarafından dikkatli bir şekilde incelenerek tümör tespiti yapılır. Bu işlem hem iş yükü yoğunluğuna hem de sübjektif bir değerlendirmeye neden olmaktadır.
Görüntülerde tümörün otomatik tespiti için International Symposium on Biomedical Image (ISBI) tarafından Camelyon16 veri seti oluşturulmuştur. Bu veri seti, hızlı bölge tabanlı evrişimli sinir ağı (Faster Region-Based Convolutional Neural Network, Faster RCNN) ve mask bölge tabanlı evrişimli sinir ağı (Mask Region-Based Convolutional Neural Network, Mask RCNN) derin öğrenme algoritmaları kullanılarak tümör tespiti ve bölütleme yapılmıştır. Tüm slayt görüntülerin farklı seviye çözünürlüklerinde oluşturulan veri setleri ile Faster RCNN ve görüntülerin 3. çözünürlük seviyeden oluşturulan farklı boyutu veri setleri ile Mask RCNN algoritmaları performansları incelenmiştir. Son olarak ISBI’da dereceye giren HMS&MIT yöntemi kısıtlı bir veri seti üzerinde uygulanarak Faster RCNN ve Mask RCNN algoritmaları ile tüm slayt görüntüsü üzerindeki başarımları kıyaslanmıştır. Tüm slayt görüntülerinin analizinde Mask RCNN (%57) görüntülerin düşük çözünürlük seviyesinde (3. seviye) çalışmış olmasına rağmen yüksek çözünürlük seviyesinde (0. seviye) çalışan HMS&MIT (%58) yöntemine yakın bir doğruluk (AUC) değeri almaktadır.

Anahtar Kelimeler

Görüntü Bölütleme, Derin Ögrenme, Faster RCNN, Mask RCNN, Medikal Görüntü Bölütleme, Nesne Tespiti, Tümör Tespiti

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